Team
Julia Neidhardt
Researcher & Lab Director
Julia Neidhardt is a researcher at the Research Unit E-Commerce at TU Wien informatics with a background in mathematics and computer science. Previously she was a guest researcher at the Austrian Academy of Sciences as well as visiting scholar at Northwestern University, USA, and at the University of Geneva, Switzerland. Her research focuses on user modeling and recommender systems in tourism and in the news domain, developing approaches to capture online opinion-forming and online behavior, and digital humanities. Her research is published in highly renowned conference proceedings and journals including Nature Human Behaviour. She regularly is invited to give talks on topics related to her research, among others at the Oxford Women in Computer Science – Distinguished Speaker Series at the University of Oxford. Julia Neidhardt is a senior program committee member of the ACM Conference on Recommender Systems (RecSys) and is an associate editor of the Journal of Information Technology &Tourism as well as a distinguished reviewer of the newly established journal ACM Transactions on Recommender Systems (TORS). She was research track co-chair of ENTER 2020 and ENTER 2019 and a co-organizer of a number of workshops and conferences. Julia Neidhardt is part of the Digital Humanism Initiative at TU Wien and board member of Center for Artificial Intelligence and Machine Learning (CAIML). She will lead the CD Lab for Recommender Systems, applying her expertise in these various research areas focusing on recommender systems.
Supervisions
- Analysis of the Performance of a Conversational Recommender System in Online Fashion E-Commerce (in progress). Bachelor’s Thesis.
- Analysing Dynamics Over Time of Bias in Recommender Systems (in progress). Boris Staykov. Master’s Thesis.
- Automated Fine-Grained Location Tagging in Online News (in progress). Master’s Thesis.
- Data Transformation Tool to Explore News Recommenders (in progress). Manuel Feuerstein. Bachelor’s Thesis.
- Advancing Session-Based Recommendations: Integrating Modern LLMs and Micro-Behaviors (in progress). Florian Dedov. Master’s Thesis.
- Intent Discovery and Product Search for Price Comparison Sites (in progress). Master’s Thesis.
- POI Recommender Systems for Customized Walking Tours Using Context and Category (in progress). Joanna Zamiechowska. Master’s Thesis.
- Enhancing Video Segment Discovery in Educational Content: A Conversational Approach with Retrieval-Augmented Generation (in progress). Dragana Naceva. Master’s Thesis.
- The Role of Clarifying Questions in Conversational Recommender Systems: Balancing Explicitness (in progress). Anna Baghumyan. Master’s Thesis.
- Evaluating Agentic Retrieval Augmented Generation in Open Tender Evaluations (in progress). Franz Ottitsch. Master’s Thesis.
- Examining Tech Bias: Analyzing Career Recommendations in LLMs through Demographic Persona-Based Prompting (completed). 2026. Bachelor’s Thesis.
- The Impact of Using Large Language Models on the Performance of Recommender Systems (completed). Michael Schmiedmayer. 2026. Master’s Thesis.
- Bias in Medical Recommendations: Prompting vs. Fine-tuning of Large Language Models (completed). Daha Pavlovic. 2026. Master’s Thesis.
- Data-Centric AI for Conceptual Modeling: Cleansed Data and GNN-LLM based Recommender for UML Class Diagrams (completed). Andjela Djelic. 2026. Master’s Thesis.
- Temporal Analysis of Session Clusters in Clickstream Data from a Price Comparison Platform (completed). Luca Turin. 2026. Bachelor’s Thesis.
- Exploration of Intermediate Fusion Strategies: Between Graph and Text Modalities in Session-Based Recommender Systems (completed). Roman Grebnev. 2025. Master’s Thesis.
- Choice-Based Preference Elicitation to Reduce the Cold Start Problem of a Leisure Activities Recommender in a Mobile App (completed). Andreas Fink. 2025. Master’s Thesis.
- Comparative Analysis of Fashion Captioning and Multimodal Fashion Recommendation (completed). Maria De Los Angeles Gwendolyn Aglae Rippberger Fonseca. 2025. Master’s Thesis.
- Context over Categories – Implementing the Theory of Constructed Emotion (completed). Nils Klüwer. 2025. Master’s Thesis.
- Evaluating the Fairness of News Recommender Algorithms Within Detected User Communities (completed). Bernhard Steindl. 2024. Master’s Thesis.
- Exploration of Content-Based Cross-Domain Podcast Recommender Systems (completed). Matthias Hofmaier. 2024. Master’s Thesis.
- Measuring Controversy in Online Discussions (completed). Ivan Andreev. 2024. Bachelor’s Thesis.
- Classification of users in an online news forum – a data analysis of user types and user interaction in the online forum of an Austrian newspaper (completed). Felix Scholz. 2023. Master’s Thesis.
- Network Analysis on the Austrian Media Corpus: Examining measures of co-occurrence between entities in Austrian media (completed). Gabriel Grill. 2023. Master’s Thesis.
- Wie wirken sich technologische Filterblasen, als Folge der algorithmisch personalisierten Aufbereitung von Inhalten sozialer Netzwerke, auf die politische Meinungspolarisierung der Nutzer:innen aus? (completed). Nadine Chincea. 2023. Bachelor’s Thesis.
- How Can Digital Nudges Guide Users Towards More Diverse News Consumption? (completed). Laura Valentina Modre. 2023. Bachelor’s Thesis.
- Diversity in News Recommendations- Towards a Broader Perspective on Diversity-Aware-Recommender-Systems and Their Influence on Users (completed). Linda Basso. 2023. Bachelor’s Thesis.
- Improving Trust in Recommender Systems through Context Clues (completed). Tobias Sippl. 2023. Bachelor’s Thesis.
- Exploring the Sentiment Patterns of Clustered COVID-19 News Articles on Mask Requirements: A Comparison with User Comments (completed). Lukas Burtscher. 2023. Bachelor’s Thesis.
- Content-Based Restaurant Recommendation Systems Using Textual and Visual Data (completed). Dante Godolja. 2023. Bachelor’s Thesis.
- Exploring Group Fairness in News Media Recommendations: Algorithms, Metrics, and Grouping (completed). Blake Huebner. 2023. Master’s Thesis.
- COVID-19 and Populism in Austrian News User Comments – A Machine Learning Approach (completed). Ahmadou Wagne. 2023. Master’s Thesis.
- K-Means Clustering of Fashion Behavior: A Language-Focused Approach (completed). Florian Dedov. 2022. Bachelor’s Thesis.
- Merging the Gap Between Automated and Human Centered Usability Testing (completed). Can Özgür Yilmaz. 2022. Master’s Thesis.
- Exhibit Rating Prediction and Visitor Path Prediction in a Museum Setting (completed). Marek Furka. 2022. Master’s Thesis.
- Dynamic Sentiment Analysis for Measuring Media Bias (completed). Thomas E. Kolb. 2022. Master’s Thesis.
Publications
2026
Neidhardt, Julia; Kolb, Thomas Elmar; Wagne, Ahmadou; Rippberger, Gwendolyn; Baeza-Yates, Ricardo
When Should Recommender Systems Not Act? Proceedings Article Forthcoming
In: Proceedings of the 20th ACM Conference on Recommender Systems (RecSys '26), Association for Computing Machinery, New York, NY, USA, Forthcoming.
@inproceedings{neidhardt2026when,
title = {When Should Recommender Systems Not Act?},
author = {Julia Neidhardt and Thomas Elmar Kolb and Ahmadou Wagne and Gwendolyn Rippberger and Ricardo Baeza-Yates},
year = {2026},
date = {2026-09-01},
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Modre, Laura; Wagne, Ahmadou; Kolb, Thomas Elmar; Neidhardt, Julia
Interaction Modality and Trust: Investigating System-Driven Conversational Recommendation and Faceted Search Proceedings Article Forthcoming
In: Proceedings of the 20th ACM Conference on Recommender Systems, Association for Computing Machinery, New York, NY, USA, Forthcoming.
@inproceedings{Modre2026,
title = {Interaction Modality and Trust: Investigating System-Driven Conversational Recommendation and Faceted Search},
author = {Laura Modre and Ahmadou Wagne and Thomas Elmar Kolb and Julia Neidhardt},
year = {2026},
date = {2026-08-24},
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Wagne, Ahmadou; Kolb, Thomas; Banerjee, Ashmi; Nazary, Fatemeh; Neidhardt, Julia; Deldjoo, Yashar
Conversational Recommender Systems Using Generative Models (Gen-CRS): A Literature Review Journal Article
In: ACM Trans. Recomm. Syst., 2026, ISSN: 2770-6699.
@article{Wagne2026,
title = {Conversational Recommender Systems Using Generative Models (Gen-CRS): A Literature Review},
author = {Ahmadou Wagne and Thomas Kolb and Ashmi Banerjee and Fatemeh Nazary and Julia Neidhardt and Yashar Deldjoo},
doi = {10.1145/3828551},
issn = {2770-6699},
year = {2026},
date = {2026-07-07},
journal = {ACM Trans. Recomm. Syst.},
publisher = {Association for Computing Machinery (ACM)},
abstract = {Generative models are profoundly impacting how conversational recommender systems (CRS) are conceptualized, designed, and deployed in practice, enabling mixed-initiative, context-aware, and tool-augmented recommendation workflows that go beyond rigid traditional pipelines. This survey provides a comprehensive review of Generative Conversational Recommender Systems (Gen-CRS) from 2018–2025. We organize the field into three analytical layers: (i) architectural and methodological foundations, covering unified, modular, and agentic system designs, strategies for adapting foundation models, and methods for recommendation and response generation, (ii) knowledge and data foundations, detailing how item-level information (structured catalogs, knowledge graphs, unstructured and multi-modal content), user-level signals (short-term preferences, long-term profiles, and persona representations), and dialogue corpora and logs are integrated into generative workflows, and (iii) evaluation methodologies, structured around what is evaluated (output types), which quality dimensions are measured (e.g., task effectiveness, conversational quality, efficiency, user trust, and ethical concerns), how evaluation is conducted (offline metrics, simulation, user studies, online testing), who evaluates (humans, automated metrics, LLM-based judges), and which stakeholders are considered (consumers, item providers, platforms and society). Our analysis highlights both the capabilities and the risks introduced by generative components, including challenges in grounding, catalog fidelity, controllability, safety, and bias. We argue for responsible and transparent system design, emphasizing validated knowledge integration and evaluation protocols that reflect the full complexity of conversational interactions and system behaviors, and we outline open research challenges that must be addressed to develop reliable and trustworthy Gen-CRS. This survey is also informed by, and partly the result of, tutorial feedback we collected at ACM RecSys 2025 in Prague [42]. },
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Sergaš, Uroš; Wagne, Ahmadou; Kolb, Thomas Elmar; Neidhardt, Julia; Ferwerda, Bruce; Tkalcic, Marko
Prompt to Press: Evaluating Human Perception of AI Involvement in News Writing Across Prompt Specificity Proceedings Article
In: Companion Proceedings of the 31st International Conference on Intelligent User Interfaces, pp. 89–92, Association for Computing Machinery, New York, NY, USA, 2026, ISBN: 9798400719851.
@inproceedings{10.1145/3742414.3795097,
title = {Prompt to Press: Evaluating Human Perception of AI Involvement in News Writing Across Prompt Specificity},
author = {Uroš Sergaš and Ahmadou Wagne and Thomas Elmar Kolb and Julia Neidhardt and Bruce Ferwerda and Marko Tkalcic},
url = {https://doi.org/10.1145/3742414.3795097},
doi = {10.1145/3742414.3795097},
isbn = {9798400719851},
year = {2026},
date = {2026-01-01},
booktitle = {Companion Proceedings of the 31st International Conference on Intelligent User Interfaces},
pages = {89–92},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {IUI '26 Companion},
abstract = {Large language models (LLMs) are becoming a common feature in content creation tools, prompting important questions about how design choices influence user trust and engagement in AI-assisted journalism. Beyond output quality, factors such as prompt specificity, model choice, and authorship disclosure are themselves interaction design parameters that influence how users interpret and evaluate AI contributions. Yet, little is known about how these design decisions affect reader perceptions in journalistic contexts. To address this gap, we conducted an experiment with 150 participants who evaluated news articles on the sensitive topic of assisted suicide. The articles systematically varied in authorship (human-written, AI-edited, or AI-generated), stance (pro- or anti-legalization), and prompt specificity (vague, moderate, or highly detailed). Participants rated each article on engagement, subjectivity, and perceived AI involvement, and also provided open-ended justifications for their authorship judgments. Our findings show that prompt specificity and model choice significantly influence perceptions of authorship, underscoring how technical design decisions in AI tools can shape public trust in journalism.},
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Large language models (LLMs) are becoming a common feature in content creation tools, prompting important questions about how design choices influence user trust and engagement in AI-assisted journalism. Beyond output quality, factors such as prompt specificity, model choice, and authorship disclosure are themselves interaction design parameters that influence how users interpret and evaluate AI contributions. Yet, little is known about how these design decisions affect reader perceptions in journalistic contexts. To address this gap, we conducted an experiment with 150 participants who evaluated news articles on the sensitive topic of assisted suicide. The articles systematically varied in authorship (human-written, AI-edited, or AI-generated), stance (pro- or anti-legalization), and prompt specificity (vague, moderate, or highly detailed). Participants rated each article on engagement, subjectivity, and perceived AI involvement, and also provided open-ended justifications for their authorship judgments. Our findings show that prompt specificity and model choice significantly influence perceptions of authorship, underscoring how technical design decisions in AI tools can shape public trust in journalism.
Wagne, Ahmadou; Kolb, Thomas Elmar; Banerjee, Ashmi; Neidhardt, Julia; Deldjoo, Yashar
LLM4Good: The 2nd Workshop on Sustainable and Trustworthy Large Language Models for Personalization Proceedings Article
In: Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization, pp. 684–687, Association for Computing Machinery, New York, NY, USA, 2026, ISBN: 9798400723117.
@inproceedings{10.1145/3774935.3802536,
title = {LLM4Good: The 2nd Workshop on Sustainable and Trustworthy Large Language Models for Personalization},
author = {Ahmadou Wagne and Thomas Elmar Kolb and Ashmi Banerjee and Julia Neidhardt and Yashar Deldjoo},
url = {https://doi.org/10.1145/3774935.3802536},
doi = {10.1145/3774935.3802536},
isbn = {9798400723117},
year = {2026},
date = {2026-01-01},
booktitle = {Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization},
pages = {684–687},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {UMAP '26},
abstract = {Large Language Models (LLMs) are transforming personalized services by enabling adaptive, context-aware recommendations and interactions. However, deploying these models at scale raises significant concerns about environmental impact, fairness, privacy, and trustworthiness, including high energy consumption, biased outputs, privacy breaches, and hallucinations. The LLM4Good workshop was already hosted at UMAP’251 and is a half-day workshop that addresses these challenges by fostering dialogue on sustainable and ethical approaches to LLM-based personalization. Participants will explore energy-efficient techniques, bias mitigation, privacy-preserving methods, and responsible deployment strategies. The workshop aligns with Sustainable Development Goals and Digital Humanism principles. It aims to guide the development of trustworthy, human-centric LLM systems that positively impact education, healthcare, and other domains.},
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Large Language Models (LLMs) are transforming personalized services by enabling adaptive, context-aware recommendations and interactions. However, deploying these models at scale raises significant concerns about environmental impact, fairness, privacy, and trustworthiness, including high energy consumption, biased outputs, privacy breaches, and hallucinations. The LLM4Good workshop was already hosted at UMAP’251 and is a half-day workshop that addresses these challenges by fostering dialogue on sustainable and ethical approaches to LLM-based personalization. Participants will explore energy-efficient techniques, bias mitigation, privacy-preserving methods, and responsible deployment strategies. The workshop aligns with Sustainable Development Goals and Digital Humanism principles. It aims to guide the development of trustworthy, human-centric LLM systems that positively impact education, healthcare, and other domains.
Nalis, Irina; Klüwer, Nils; Neidhardt, Julia
From Labels to Context: Context-Sensitive User Modeling of Affect with Large Language Models Proceedings Article
In: Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization, pp. 490–493, Association for Computing Machinery, New York, NY, USA, 2026, ISBN: 9798400723117.
@inproceedings{10.1145/3774935.3812706,
title = {From Labels to Context: Context-Sensitive User Modeling of Affect with Large Language Models},
author = {Irina Nalis and Nils Klüwer and Julia Neidhardt},
url = {https://doi.org/10.1145/3774935.3812706},
doi = {10.1145/3774935.3812706},
isbn = {9798400723117},
year = {2026},
date = {2026-01-01},
booktitle = {Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization},
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abstract = {Emotion-aware user modeling is increasingly important in adaptive systems, yet many approaches still represent affect as a fixed label inferred from isolated inputs. This limits their ability to capture how emotional meaning develops across context, time, and interaction history. We address this limitation by modeling affect as a user-level, context-dependent phenomenon. We introduce the context sphere, a structured user representation that aggregates interaction history and preserves conversational and temporal context. Building on this representation, we use a guided two-stage Large Language Model (LLM) pipeline to generate affective interpretations over extended interaction context. We evaluate the approach using automated consistency checks and an exploratory qualitative study with five semi-structured interviews. Results provide initial evidence of plausible, interpretable context-sensitive affective interpretation.},
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Emotion-aware user modeling is increasingly important in adaptive systems, yet many approaches still represent affect as a fixed label inferred from isolated inputs. This limits their ability to capture how emotional meaning develops across context, time, and interaction history. We address this limitation by modeling affect as a user-level, context-dependent phenomenon. We introduce the context sphere, a structured user representation that aggregates interaction history and preserves conversational and temporal context. Building on this representation, we use a guided two-stage Large Language Model (LLM) pipeline to generate affective interpretations over extended interaction context. We evaluate the approach using automated consistency checks and an exploratory qualitative study with five semi-structured interviews. Results provide initial evidence of plausible, interpretable context-sensitive affective interpretation.
Benson, Juliane; Zeh, Katharina; Essfors, Hannes; Fellner, Hannes; Neidhardt, Julia; Baumann, Andreas
Linguistic Diversity and Digitalization: An Ambivalent Relationship Proceedings Article
In: Hagedorn, Ludger; Schmid, Ute; Winter, Susan; Woltran, Stefan (Ed.): Digital Humanism, pp. 358–365, Springer Nature Switzerland, Cham, 2026, ISBN: 978-3-032-11108-1.
@inproceedings{10.1007/978-3-032-11108-1_26,
title = {Linguistic Diversity and Digitalization: An Ambivalent Relationship},
author = {Juliane Benson and Katharina Zeh and Hannes Essfors and Hannes Fellner and Julia Neidhardt and Andreas Baumann},
editor = {Ludger Hagedorn and Ute Schmid and Susan Winter and Stefan Woltran},
isbn = {978-3-032-11108-1},
year = {2026},
date = {2026-01-01},
booktitle = {Digital Humanism},
pages = {358–365},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {In this position paper, we argue that while digitalization amplifies biases towards only few languages dominating the linguistic landscape, modern language technology can help to mitigate language loss. We first elaborate on how the linguistic landscape in the digital and the non-digital sphere are distributionally different from each other in that the latter is strongly biased towards English, at the same time under-representing thousands of languages and the cultural knowledge that they encode. In a second step, we present results of qualitative interviews on individual linguistic experiences in the digital and the non-digital sphere that we have conducted in Québec, one of the provinces of Canada known for its linguistic diversity. These interviews highlight the potential that modern language technology have for safeguarding linguistic diversity. We conclude that the study of the impact of digitalization on the global linguistic landscape not only requires differential ways of measuring linguistic diversity but also a nuanced operationalization of digitalization.},
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In this position paper, we argue that while digitalization amplifies biases towards only few languages dominating the linguistic landscape, modern language technology can help to mitigate language loss. We first elaborate on how the linguistic landscape in the digital and the non-digital sphere are distributionally different from each other in that the latter is strongly biased towards English, at the same time under-representing thousands of languages and the cultural knowledge that they encode. In a second step, we present results of qualitative interviews on individual linguistic experiences in the digital and the non-digital sphere that we have conducted in Québec, one of the provinces of Canada known for its linguistic diversity. These interviews highlight the potential that modern language technology have for safeguarding linguistic diversity. We conclude that the study of the impact of digitalization on the global linguistic landscape not only requires differential ways of measuring linguistic diversity but also a nuanced operationalization of digitalization.
2025
Pachinger, Pia; Goldzycher, Janis; Planitzer, Anna M.; Neidhardt, Julia; Hanbury, Allan
A Disaggregated Dataset on English Offensiveness Containing Spans Proceedings Article
In: Abercrombie, Gavin; Basile, Valerio; Frenda, Simona; Tonelli, Sara; Dudy, Shiran (Ed.): Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP, pp. 1–14, Association for Computational Linguistics, Suzhou, China, 2025, ISBN: 979-8-89176-350-0.
@inproceedings{pachinger-etal-2025-disaggregated,
title = {A Disaggregated Dataset on English Offensiveness Containing Spans},
author = {Pia Pachinger and Janis Goldzycher and Anna M. Planitzer and Julia Neidhardt and Allan Hanbury},
editor = {Gavin Abercrombie and Valerio Basile and Simona Frenda and Sara Tonelli and Shiran Dudy},
url = {https://aclanthology.org/2025.nlperspectives-1.1/},
doi = {10.18653/v1/2025.nlperspectives-1.1},
isbn = {979-8-89176-350-0},
year = {2025},
date = {2025-11-01},
booktitle = {Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP},
pages = {1–14},
publisher = {Association for Computational Linguistics},
address = {Suzhou, China},
abstract = {Toxicity labels at sub-document granularity and disaggregated labels lead to more nuanced and personalized toxicity classification and facilitate analysis. We re-annotate a subset of 1983 posts of the Jigsaw Toxic Comment Classification Challenge and provide disaggregated toxicity labels and spans that identify inappropriate language and targets of toxic statements. Manual analysis shows that five annotations per instance effectively capture meaningful disagreement patterns and allow for finer distinctions between genuine disagreement and that arising from annotation error or inconsistency. Our main findings are: (1) Disagreement often stems from divergent interpretations of edge-case toxicity (2) Disagreement is especially high in cases of toxic statements involving non-human targets (3) Disagreement on whether a passage consists of inappropriate language occurs not only on inherently questionable terms, but also on words that may be inappropriate in specific contexts while remaining acceptable in others (4) Transformer-based models effectively learn from aggregated data that reduces false negative classifications by being more sensitive towards minority opinions for posts to be toxic. We publish the new annotations under the CC BY 4.0 license.},
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Toxicity labels at sub-document granularity and disaggregated labels lead to more nuanced and personalized toxicity classification and facilitate analysis. We re-annotate a subset of 1983 posts of the Jigsaw Toxic Comment Classification Challenge and provide disaggregated toxicity labels and spans that identify inappropriate language and targets of toxic statements. Manual analysis shows that five annotations per instance effectively capture meaningful disagreement patterns and allow for finer distinctions between genuine disagreement and that arising from annotation error or inconsistency. Our main findings are: (1) Disagreement often stems from divergent interpretations of edge-case toxicity (2) Disagreement is especially high in cases of toxic statements involving non-human targets (3) Disagreement on whether a passage consists of inappropriate language occurs not only on inherently questionable terms, but also on words that may be inappropriate in specific contexts while remaining acceptable in others (4) Transformer-based models effectively learn from aggregated data that reduces false negative classifications by being more sensitive towards minority opinions for posts to be toxic. We publish the new annotations under the CC BY 4.0 license.
Steindl, Bernhard; Kolb, Thomas Elmar; Neidhardt, Julia
Beyond Demographics: Evaluating News Recommender Systems Fairness Through Behavioural Communities Proceedings Article
In: Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, pp. 13–17, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 9798400713996.
@inproceedings{10.1145/3708319.3733694,
title = {Beyond Demographics: Evaluating News Recommender Systems Fairness Through Behavioural Communities},
author = {Bernhard Steindl and Thomas Elmar Kolb and Julia Neidhardt},
url = {https://doi.org/10.1145/3708319.3733694},
doi = {10.1145/3708319.3733694},
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year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {13–17},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {UMAP Adjunct '25},
abstract = {Fairness in recommender systems is often framed around demographic attributes. In this work, we explore a novel direction—evaluating fairness across latent behavioural communities derived from user interactions on a real-world news platform. Using graph-based community detection (Louvain and Infomap), we identify large user groups and examine how different network modelling choices affect fairness outcomes in both traditional and fairness-aware recommender systems. Experiments on an Austrian news dataset reveal that small changes in graph construction considerably impact community formation and recommendation quality. Notably, fairness-aware algorithms show only marginal improvements over standard approaches, underscoring the complexity of achieving equitable outcomes in real-world systems and raising important questions for future research.},
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Fairness in recommender systems is often framed around demographic attributes. In this work, we explore a novel direction—evaluating fairness across latent behavioural communities derived from user interactions on a real-world news platform. Using graph-based community detection (Louvain and Infomap), we identify large user groups and examine how different network modelling choices affect fairness outcomes in both traditional and fairness-aware recommender systems. Experiments on an Austrian news dataset reveal that small changes in graph construction considerably impact community formation and recommendation quality. Notably, fairness-aware algorithms show only marginal improvements over standard approaches, underscoring the complexity of achieving equitable outcomes in real-world systems and raising important questions for future research.
Kolb, Thomas Elmar; Wagne, Ahmadou; Banerjee, Ashmi; Nazary, Fatemeh; Neidhardt, Julia; Deldjoo, Yashar; Noia, Tommaso Di
A Tutorial on Recent Advances in Generative Conversational Recommender Systems Proceedings Article
In: Proceedings of the Nineteenth ACM Conference on Recommender Systems, pp. 1420–1422, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 9798400713644.
@inproceedings{10.1145/3705328.3748010,
title = {A Tutorial on Recent Advances in Generative Conversational Recommender Systems},
author = {Thomas Elmar Kolb and Ahmadou Wagne and Ashmi Banerjee and Fatemeh Nazary and Julia Neidhardt and Yashar Deldjoo and Tommaso Di Noia},
url = {https://doi.org/10.1145/3705328.3748010},
doi = {10.1145/3705328.3748010},
isbn = {9798400713644},
year = {2025},
date = {2025-01-01},
booktitle = {Proceedings of the Nineteenth ACM Conference on Recommender Systems},
pages = {1420–1422},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {RecSys '25},
abstract = {Conversational recommender systems (CRSs) are increasingly vital for delivering multi-turn, context-aware recommendations. This tutorial provides a concise yet comprehensive exploration of modern generative CRSs, highlighting recent advances in generative AI—such as breakthroughs in large language models and neural generation pipelines, that enhance dialogue management, user modeling, and response generation. In addition, the tutorial addresses core challenges, including data acquisition, multi-turn personalization, and evaluation issues, such as controlling hallucinations, accounting for social factors, and managing ethical considerations, while also discussing emerging risks and novel solutions. Ultimately, participants will be equipped with actionable insights and practical tools for building new conversational recommender systems powered by generative models.},
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Conversational recommender systems (CRSs) are increasingly vital for delivering multi-turn, context-aware recommendations. This tutorial provides a concise yet comprehensive exploration of modern generative CRSs, highlighting recent advances in generative AI—such as breakthroughs in large language models and neural generation pipelines, that enhance dialogue management, user modeling, and response generation. In addition, the tutorial addresses core challenges, including data acquisition, multi-turn personalization, and evaluation issues, such as controlling hallucinations, accounting for social factors, and managing ethical considerations, while also discussing emerging risks and novel solutions. Ultimately, participants will be equipped with actionable insights and practical tools for building new conversational recommender systems powered by generative models.
Kolb, Thomas Elmar; Nalis, Irina; Neidhardt, Julia
Bridging Preferences: Multi-Stakeholder Insights on Ideal News Recommendations Proceedings Article
In: Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, pp. 268–272, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 9798400713132.
@inproceedings{10.1145/3699682.3728355,
title = {Bridging Preferences: Multi-Stakeholder Insights on Ideal News Recommendations},
author = {Thomas Elmar Kolb and Irina Nalis and Julia Neidhardt},
url = {https://doi.org/10.1145/3699682.3728355},
doi = {10.1145/3699682.3728355},
isbn = {9798400713132},
year = {2025},
date = {2025-01-01},
booktitle = {Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {268–272},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {UMAP '25},
abstract = {In the evolving realm of recommender systems, our study contributes to the understanding of potential improvements in news recommendation beyond accuracy. Central to our research is the integration of insights from news industry experts and prospective readers, compared with automated news recommendations. We conducted a labeling study with 168 articles, using Best-Worst Scaling (BWS) for ranking and topic modeling. This approach enabled a thorough examination of stakeholder expectations for ideal reading recommendations, specifically by investigating the gap between stated and revealed preferences. Our findings show alignment in ranking behavior among journalists, prospective readers, and the BM-25 algorithm. However, preferences for different beyond-accuracy measures varied. Accompanying this work, a corpus of news articles and the labeled rankings have been made available.},
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In the evolving realm of recommender systems, our study contributes to the understanding of potential improvements in news recommendation beyond accuracy. Central to our research is the integration of insights from news industry experts and prospective readers, compared with automated news recommendations. We conducted a labeling study with 168 articles, using Best-Worst Scaling (BWS) for ranking and topic modeling. This approach enabled a thorough examination of stakeholder expectations for ideal reading recommendations, specifically by investigating the gap between stated and revealed preferences. Our findings show alignment in ranking behavior among journalists, prospective readers, and the BM-25 algorithm. However, preferences for different beyond-accuracy measures varied. Accompanying this work, a corpus of news articles and the labeled rankings have been made available.
Kolb, Thomas Elmar; Banerjee, Ashmi; Wagne, Ahmadou; Neidhardt, Julia; Deldjoo, Yashar
LLM4Good: The 1st Workshop on Sustainable and Trustworthy Large Language Models for Personalization Proceedings Article
In: Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, pp. 385–387, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 9798400713996.
@inproceedings{10.1145/3708319.3727560,
title = {LLM4Good: The 1st Workshop on Sustainable and Trustworthy Large Language Models for Personalization},
author = {Thomas Elmar Kolb and Ashmi Banerjee and Ahmadou Wagne and Julia Neidhardt and Yashar Deldjoo},
url = {https://doi.org/10.1145/3708319.3727560},
doi = {10.1145/3708319.3727560},
isbn = {9798400713996},
year = {2025},
date = {2025-01-01},
booktitle = {Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization},
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publisher = {Association for Computing Machinery},
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abstract = {Large Language Models (LLMs) are transforming personalized services by enabling adaptive, context-aware recommendations and interactions. However, deploying these models at scale raises significant concerns about environmental impact, fairness, privacy, and trustworthiness, including high energy consumption, biased outputs, privacy breaches, and hallucinations. The LLM4Good workshop is a half-day workshop that addresses these challenges by fostering dialogue on sustainable and ethical approaches to LLM-based personalization. Participants will explore energy-efficient techniques, bias mitigation, privacy-preserving methods, and responsible deployment strategies. The workshop aligns with Sustainable Development Goals and Digital Humanism principles. It aims to guide the development of trustworthy, human-centric LLM systems that positively impact education, healthcare, and other domains.},
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Large Language Models (LLMs) are transforming personalized services by enabling adaptive, context-aware recommendations and interactions. However, deploying these models at scale raises significant concerns about environmental impact, fairness, privacy, and trustworthiness, including high energy consumption, biased outputs, privacy breaches, and hallucinations. The LLM4Good workshop is a half-day workshop that addresses these challenges by fostering dialogue on sustainable and ethical approaches to LLM-based personalization. Participants will explore energy-efficient techniques, bias mitigation, privacy-preserving methods, and responsible deployment strategies. The workshop aligns with Sustainable Development Goals and Digital Humanism principles. It aims to guide the development of trustworthy, human-centric LLM systems that positively impact education, healthcare, and other domains.
Rippberger, Gwendolyn; Neidhardt, Julia
Comparative Analysis of Fashion Captioning for Multimodal Fashion Recommendation Proceedings Article
In: Pomo, Claudio; Jannach, Dietmar; Kim, Yubin; Malitesta, Daniele; Mancino, Alberto Carlo Maria; McAuley, Julian J.; Melchiorre, Alessandro B.; Nawaz, Shah (Ed.): Proceedings of the DaQuaMRec 2025 Workshop on Data Quality-Aware Multimodal Recommendation co-located with RecSys 2025, Prague, Czech Republic, September 22, 2025, pp. 8–19, CEUR-WS.org, 2025.
@inproceedings{DBLP:conf/daquamrec/RippbergerN25,
title = {Comparative Analysis of Fashion Captioning for Multimodal Fashion
Recommendation},
author = {Gwendolyn Rippberger and Julia Neidhardt},
editor = {Claudio Pomo and Dietmar Jannach and Yubin Kim and Daniele Malitesta and Alberto Carlo Maria Mancino and Julian J. McAuley and Alessandro B. Melchiorre and Shah Nawaz},
url = {https://ceur-ws.org/Vol-4188/paper1.pdf},
year = {2025},
date = {2025-01-01},
booktitle = {Proceedings of the DaQuaMRec 2025 Workshop on Data Quality-Aware Multimodal
Recommendation co-located with RecSys 2025, Prague, Czech Republic,
September 22, 2025},
pages = {8–19},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
keywords = {},
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Delić, Anđela; Ali, Syed Juned; Verbruggen, Charlotte; Neidhardt, Julia; Bork, Dominik
A Model Cleansing Pipeline for Model-Driven Engineering: Mitigating the Garbage In, Garbage Out Problem for Open Model Repositories Proceedings Article
In: 2025 ACM/IEEE 28th International Conference on Model Driven Engineering Languages and Systems (MODELS), pp. 60-71, 2025.
@inproceedings{11245421,
title = {A Model Cleansing Pipeline for Model-Driven Engineering: Mitigating the Garbage In, Garbage Out Problem for Open Model Repositories},
author = {Anđela Delić and Syed Juned Ali and Charlotte Verbruggen and Julia Neidhardt and Dominik Bork},
doi = {10.1109/MODELS67397.2025.00012},
year = {2025},
date = {2025-01-01},
booktitle = {2025 ACM/IEEE 28th International Conference on Model Driven Engineering Languages and Systems (MODELS)},
pages = {60-71},
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}
Neidhardt, Julia; Kuflik, Tsvi; Livne, Amit; Zanker, Markus; Wörndl, Wolfgang
Workshop on Recommenders in Tourism (RecTour) 2025 Proceedings Article
In: Proceedings of the Nineteenth ACM Conference on Recommender Systems, pp. 1412–1413, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 9798400713644.
@inproceedings{10.1145/3705328.3748500,
title = {Workshop on Recommenders in Tourism (RecTour) 2025},
author = {Julia Neidhardt and Tsvi Kuflik and Amit Livne and Markus Zanker and Wolfgang Wörndl},
url = {https://doi.org/10.1145/3705328.3748500},
doi = {10.1145/3705328.3748500},
isbn = {9798400713644},
year = {2025},
date = {2025-01-01},
booktitle = {Proceedings of the Nineteenth ACM Conference on Recommender Systems},
pages = {1412–1413},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {RecSys '25},
abstract = {The Workshop on Recommenders in Tourism (RecTour) has been successfully held in conjunction with the ACM Conference on Recommender Systems (RecSys) since 2016, with the exception of one year. This workshop focuses on the unique and evolving challenges of recommender systems in the tourism domain. Over time, RecTour has fostered an active community supported by both academia and industry. In this overview paper, we outline our motivations for organizing the RecTour workshop and highlight the main topics covered in RecTour submissions, including destination recommendation, privacy concerns in travel recommender systems, the cold-start problem, transformer-based approaches in recommendation systems, and best practices for evaluation and experimentation.},
keywords = {},
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The Workshop on Recommenders in Tourism (RecTour) has been successfully held in conjunction with the ACM Conference on Recommender Systems (RecSys) since 2016, with the exception of one year. This workshop focuses on the unique and evolving challenges of recommender systems in the tourism domain. Over time, RecTour has fostered an active community supported by both academia and industry. In this overview paper, we outline our motivations for organizing the RecTour workshop and highlight the main topics covered in RecTour submissions, including destination recommendation, privacy concerns in travel recommender systems, the cold-start problem, transformer-based approaches in recommendation systems, and best practices for evaluation and experimentation.
Burke, Robin; Adomavicius, Gediminas; Bogers, Toine; Noia, Tommaso Di; Kowald, Dominik; Neidhardt, Julia; Özgöbek, Özlem; Pera, Maria Soledad; Tintarev, Nava; Ziegler, Jürgen
De-centering the (Traditional) user: Multistakeholder evaluation of recommender systems Journal Article
In: International Journal of Human-Computer Studies, vol. 203, pp. 103560, 2025, ISSN: 1071-5819.
@article{BURKE2025103560,
title = {De-centering the (Traditional) user: Multistakeholder evaluation of recommender systems},
author = {Robin Burke and Gediminas Adomavicius and Toine Bogers and Tommaso Di Noia and Dominik Kowald and Julia Neidhardt and Özlem Özgöbek and Maria Soledad Pera and Nava Tintarev and Jürgen Ziegler},
url = {https://www.sciencedirect.com/science/article/pii/S107158192500117X},
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abstract = {Multistakeholder recommender systems are those that account for the impacts and preferences of multiple groups of individuals, not just the end users receiving recommendations. Due to their complexity, these systems cannot be evaluated strictly by the overall utility of a single stakeholder, as is often the case of more mainstream recommender system applications. In this article, we focus our discussion on the challenges of multistakeholder evaluation of recommender systems. We bring attention to the different aspects involved—from the range of stakeholders involved (including but not limited to providers and consumers) to the values and specific goals of each relevant stakeholder. We discuss how to move from theoretical principles to practical implementation, providing specific use case examples. Finally, we outline open research directions for the RecSys community to explore. We aim to provide guidance to researchers and practitioners about incorporating these complex and domain-dependent issues of evaluation in the course of designing, developing, and researching applications with multistakeholder aspects.},
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Multistakeholder recommender systems are those that account for the impacts and preferences of multiple groups of individuals, not just the end users receiving recommendations. Due to their complexity, these systems cannot be evaluated strictly by the overall utility of a single stakeholder, as is often the case of more mainstream recommender system applications. In this article, we focus our discussion on the challenges of multistakeholder evaluation of recommender systems. We bring attention to the different aspects involved—from the range of stakeholders involved (including but not limited to providers and consumers) to the values and specific goals of each relevant stakeholder. We discuss how to move from theoretical principles to practical implementation, providing specific use case examples. Finally, we outline open research directions for the RecSys community to explore. We aim to provide guidance to researchers and practitioners about incorporating these complex and domain-dependent issues of evaluation in the course of designing, developing, and researching applications with multistakeholder aspects.
Klüwer, Nils; Nalis, Irina; Neidhardt, Julia
Context over Categories: Implementing the Theory of Constructed Emotion with LLM-Guided User Analysis Proceedings Article
In: Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 9798400713958.
@inproceedings{10.1145/3706599.3721205,
title = {Context over Categories: Implementing the Theory of Constructed Emotion with LLM-Guided User Analysis},
author = {Nils Klüwer and Irina Nalis and Julia Neidhardt},
url = {https://doi.org/10.1145/3706599.3721205},
doi = {10.1145/3706599.3721205},
isbn = {9798400713958},
year = {2025},
date = {2025-01-01},
booktitle = {Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {CHI EA '25},
abstract = {Emotion analysis is a critical research area with applications in content moderation and personalized systems. Many existing approaches rely on Ekman’s universal emotions theory, which reduces emotions to static categories, neglecting their complexity and contextual variability. This work introduces a novel, context-aware approach based on Lisa Feldman Barrett’s Theory of Constructed Emotion. A key contribution is the development of the “context sphere,” a personalized construct derived from user behavior data. To our knowledge, this is the first operationalization for computational methods. A context-aware emotion analysis pipeline was developed, incorporating advanced Large Language Model (LLM) prompting strategies like role-play and controlled generation. A case study in content moderation demonstrates how the “context sphere” enables contextually aware emotion analyses. Future directions include refining the framework, advancing LLM methodologies, and conducting user studies. This research lays the foundation for more human-centered, ethical, and effective emotion analysis systems.},
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Emotion analysis is a critical research area with applications in content moderation and personalized systems. Many existing approaches rely on Ekman’s universal emotions theory, which reduces emotions to static categories, neglecting their complexity and contextual variability. This work introduces a novel, context-aware approach based on Lisa Feldman Barrett’s Theory of Constructed Emotion. A key contribution is the development of the “context sphere,” a personalized construct derived from user behavior data. To our knowledge, this is the first operationalization for computational methods. A context-aware emotion analysis pipeline was developed, incorporating advanced Large Language Model (LLM) prompting strategies like role-play and controlled generation. A case study in content moderation demonstrates how the “context sphere” enables contextually aware emotion analyses. Future directions include refining the framework, advancing LLM methodologies, and conducting user studies. This research lays the foundation for more human-centered, ethical, and effective emotion analysis systems.
Pachinger, Pia; Planitzer, Anna Maria; Neidhardt, Julia; Hanbury, Allan; Lecheler, Sophie
Alignment by Disagreement? Toward Investigating LLMs’ Adaptation to Personal and Sociodemographic Variability in the Perception of Toxicity Miscellaneous
2025.
@misc{ 20.500.12708_224482,
title = {Alignment by Disagreement? Toward Investigating LLMs’ Adaptation to Personal and Sociodemographic Variability in the Perception of Toxicity},
author = {Pia Pachinger and Anna Maria Planitzer and Julia Neidhardt and Allan Hanbury and Sophie Lecheler},
year = {2025},
date = {2025-01-01},
keywords = {},
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tppubtype = {misc}
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Pachinger, Pia; Planitzer, Anna Maria; Lecheler, Sophie; Neidhardt, Julia; Hanbury, Allan; Wegener, Rebekah
A Perspectivist Approach to Content Moderation: Incorporating User Perceptions of Online Norm Violations in Toxicity Detection Models Miscellaneous
2025.
@misc{ 20.500.12708_224144,
title = {A Perspectivist Approach to Content Moderation: Incorporating User Perceptions of Online Norm Violations in Toxicity Detection Models},
author = {Pia Pachinger and Anna Maria Planitzer and Sophie Lecheler and Julia Neidhardt and Allan Hanbury and Rebekah Wegener},
year = {2025},
date = {2025-01-01},
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Zamiechowska, Joanna; Neidhardt, Julia; Wörndl, Wolfgang; Kuflik, Tsvi; Livne, Amit; Zanker, Markus
CiRi-Engine: POI Recommender System for Diverse and Balanced Walking Tours Journal Article
In: 2025.
@article{ZamiechowskaJoanna2025CPRS,
title = {CiRi-Engine: POI Recommender System for Diverse and Balanced Walking Tours},
author = {Joanna Zamiechowska and Julia Neidhardt and Wolfgang Wörndl and Tsvi Kuflik and Amit Livne and Markus Zanker},
doi = {https://doi.org/10.34726/11839},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
publisher = {CEUR Workshop Proceedings},
address = {[Erscheinungsort nicht ermittelbar]},
edition = {Version of record},
abstract = {eng: We present CiRi-Engine (CityRiddler Recommendation Engine), an interactive city walking-tour recommender system. This demonstration paper showcases a novel approach to generating personalized and balanced itineraries for urban exploration. By combining user-specified constraints, such as start and end locations, tour duration, interest categories, and challenge preferences, with an efficient dual-stage routing algorithm, CiRi-Engine dynamically constructs diverse routes featuring curated Points of Interest (POIs). The engine leverages a novel hybrid of A* and Beam Search for path planning, and incorporates preference-aware POI selection to ensure both relevance and diversity. We demonstrate firsthand how the system balances route diversity, thematic coherence, and user-specified constraints, demonstrating its effectiveness for handling multiple objectives and generating engaging walking tours.},
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eng: We present CiRi-Engine (CityRiddler Recommendation Engine), an interactive city walking-tour recommender system. This demonstration paper showcases a novel approach to generating personalized and balanced itineraries for urban exploration. By combining user-specified constraints, such as start and end locations, tour duration, interest categories, and challenge preferences, with an efficient dual-stage routing algorithm, CiRi-Engine dynamically constructs diverse routes featuring curated Points of Interest (POIs). The engine leverages a novel hybrid of A* and Beam Search for path planning, and incorporates preference-aware POI selection to ensure both relevance and diversity. We demonstrate firsthand how the system balances route diversity, thematic coherence, and user-specified constraints, demonstrating its effectiveness for handling multiple objectives and generating engaging walking tours.
2024
Burke, Robin; Adomavicius, Gediminas; Bogers, Toine; Noia, Tomasso Di; Kowald, Dominik; Neidhardt, Julia; Özgöbek, Özlem; Pera, Maria; Ziegler, Jürgen
Multistakeholder and Multimethod Evaluation Book Chapter
In: Evaluation Perspectives of Recommender Systems: Driving Research and Education (Dagstuhl Seminar 24211), vol. 14, no. 5, pp. 123–145, Schloss Dagstuhl – Leibniz-Zentrum fuer Informatik GmbH, 5, 2024.
@inbook{b077f33cc308407bb4fe6a2ee2336dfe,
title = {Multistakeholder and Multimethod Evaluation},
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abstract = {Multistakeholder recommender systems are defined by [1] as those that account for “the preferences of multiple parties when generating recommendations, especially when these parties are on different sides of the recommendation interaction.” Due to their complexity, evaluating these systems cannot be restricted to the overall utility of a single stakeholder, as is often the case of more mainstream recommender system applications. In this section, we focus our discussion on the intricacies involved in understanding what is the “right” construct required to ensure the proper evaluation of multistakeholder recommender systems. We bring attention to the different aspects involved in the evaluation of multistakeholder recommender systems – from the range of stakeholders involved (beyond producers and consumers) to the values and specific goals of each relevant stakeholder. Additionally, we discuss how to move from theoretical evaluation to practical implementation, providing specific use case examples. Finally, we outline open research directions for the RecSys community to explore. Our aim in this section is to provide guidance to researchers and practitioners about how to think about these complex and domain-dependent issues in the course of designing, developing, and researching applications with multistakeholder aspects.},
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Multistakeholder recommender systems are defined by [1] as those that account for “the preferences of multiple parties when generating recommendations, especially when these parties are on different sides of the recommendation interaction.” Due to their complexity, evaluating these systems cannot be restricted to the overall utility of a single stakeholder, as is often the case of more mainstream recommender system applications. In this section, we focus our discussion on the intricacies involved in understanding what is the “right” construct required to ensure the proper evaluation of multistakeholder recommender systems. We bring attention to the different aspects involved in the evaluation of multistakeholder recommender systems – from the range of stakeholders involved (beyond producers and consumers) to the values and specific goals of each relevant stakeholder. Additionally, we discuss how to move from theoretical evaluation to practical implementation, providing specific use case examples. Finally, we outline open research directions for the RecSys community to explore. Our aim in this section is to provide guidance to researchers and practitioners about how to think about these complex and domain-dependent issues in the course of designing, developing, and researching applications with multistakeholder aspects.
Wagne, Ahmadou; Neidhardt, Julia
Can We Integrate Items into Models? Knowledge Editing to Align LLMs with Product Catalogs Proceedings Article
In: Anelli, Vito Walter; Basile, Pierpaolo; Noia, Tommaso Di; Donini, Francesco Maria; Ferrara, Antonio; Musto, Cataldo; Narducci, Fedelucio; Ragone, Azzurra; Zanker, Markus (Ed.): Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop @ RecSys 2024, pp. 56-65, CEUR-WS.org, Bari, Italy, 2024.
@inproceedings{Wagne_Neidhardt_2024_2,
title = {Can We Integrate Items into Models? Knowledge Editing to Align LLMs with Product Catalogs},
author = {Ahmadou Wagne and Julia Neidhardt},
editor = {Vito Walter Anelli and Pierpaolo Basile and Tommaso Di Noia and Francesco Maria Donini and Antonio Ferrara and Cataldo Musto and Fedelucio Narducci and Azzurra Ragone and Markus Zanker},
year = {2024},
date = {2024-10-31},
urldate = {2024-10-31},
booktitle = {Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop @ RecSys 2024},
volume = {3817},
pages = {56-65},
publisher = {CEUR-WS.org},
address = {Bari, Italy},
abstract = {This study explores the potential of knowledge editing techniques to enhance Large Language Models (LLMs)
for Conversational Recommender Systems (CRS). While LLMs like GPT, Llama, and Gemini have advanced
conversational capabilities, they face challenges in representing dynamic, real-world item catalogs, often leading to
inaccuracies and hallucinations in recommendations. This research preliminarily investigates whether knowledge
editing can address these limitations by updating the internal knowledge of LLMs, thereby improving the
accuracy of product information without full model retraining. Using the open-source Llama2 model, we apply
two knowledge editing methods (GRACE and r-ROME) on a dataset of notebook listings. Our findings demonstrate
improvements in the model’s ability to accurately represent product features, with r-ROME achieving the highest
gain, while not decreasing model efficiency. The study highlights the perspective of utilizing knowledge editing
to enhance CRS and suggests future work to explore broader applications and impacts on recommender systems
performance},
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This study explores the potential of knowledge editing techniques to enhance Large Language Models (LLMs)
for Conversational Recommender Systems (CRS). While LLMs like GPT, Llama, and Gemini have advanced
conversational capabilities, they face challenges in representing dynamic, real-world item catalogs, often leading to
inaccuracies and hallucinations in recommendations. This research preliminarily investigates whether knowledge
editing can address these limitations by updating the internal knowledge of LLMs, thereby improving the
accuracy of product information without full model retraining. Using the open-source Llama2 model, we apply
two knowledge editing methods (GRACE and r-ROME) on a dataset of notebook listings. Our findings demonstrate
improvements in the model’s ability to accurately represent product features, with r-ROME achieving the highest
gain, while not decreasing model efficiency. The study highlights the perspective of utilizing knowledge editing
to enhance CRS and suggests future work to explore broader applications and impacts on recommender systems
performance
for Conversational Recommender Systems (CRS). While LLMs like GPT, Llama, and Gemini have advanced
conversational capabilities, they face challenges in representing dynamic, real-world item catalogs, often leading to
inaccuracies and hallucinations in recommendations. This research preliminarily investigates whether knowledge
editing can address these limitations by updating the internal knowledge of LLMs, thereby improving the
accuracy of product information without full model retraining. Using the open-source Llama2 model, we apply
two knowledge editing methods (GRACE and r-ROME) on a dataset of notebook listings. Our findings demonstrate
improvements in the model’s ability to accurately represent product features, with r-ROME achieving the highest
gain, while not decreasing model efficiency. The study highlights the perspective of utilizing knowledge editing
to enhance CRS and suggests future work to explore broader applications and impacts on recommender systems
performance
Wagne, Ahmadou; Neidhardt, Julia
What to compare? Towards understanding user sessions on price comparison platforms Proceedings Article
In: Noia, Tommaso Di; Lops, Pasquale; Joachims, Thorsten; Verbert, Katrien; Castells, Pablo; Dong, Zhenhua; London, Ben (Ed.): RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems, pp. 1158 – 1162, Association for Computing Machinery, New York, NY, United States, 2024, ISBN: 979-8-4007-1127-5.
@inproceedings{Wagne_Neidhardt_2024,
title = {What to compare? Towards understanding user sessions on price comparison platforms},
author = {Ahmadou Wagne and Julia Neidhardt},
editor = {Tommaso Di Noia and Pasquale Lops and Thorsten Joachims and Katrien Verbert and Pablo Castells and Zhenhua Dong and Ben London},
doi = {https://doi.org/10.1145/3640457.3691717},
isbn = {979-8-4007-1127-5},
year = {2024},
date = {2024-10-08},
urldate = {2024-10-08},
booktitle = {RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems},
pages = {1158 - 1162},
publisher = {Association for Computing Machinery},
address = {New York, NY, United States},
abstract = {E-commerce and online shopping have become integral to the lives of many, with various user behavior types historically identified. Beyond deciding what to buy, determining where to make a purchase has led to the importance of price comparison platforms. However, user behavior on these platforms remains underexplored. Furthermore, web analytics often struggle with tracking users over time and deriving meaningful user types from data. This paper addresses these gaps by defining session types through the analysis and clustering of user logs from a major price comparison platform. The study identifies six distinct session clusters: quick peek, major purchase, constraint-based browsing, knowledge seeking, search and browse and heavy browsing. These findings are intended to inform the design and development of a conversational recommender system (CRS). Often, CRS development occurs without adequate consideration of the existing system into which it will be integrated. The study’s findings, derived from both quantitative analysis and expert interviews, provide valuable contributions, including identified session clusters, their interpretation and indicators on which users might benefit from a CRS on these platforms.},
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E-commerce and online shopping have become integral to the lives of many, with various user behavior types historically identified. Beyond deciding what to buy, determining where to make a purchase has led to the importance of price comparison platforms. However, user behavior on these platforms remains underexplored. Furthermore, web analytics often struggle with tracking users over time and deriving meaningful user types from data. This paper addresses these gaps by defining session types through the analysis and clustering of user logs from a major price comparison platform. The study identifies six distinct session clusters: quick peek, major purchase, constraint-based browsing, knowledge seeking, search and browse and heavy browsing. These findings are intended to inform the design and development of a conversational recommender system (CRS). Often, CRS development occurs without adequate consideration of the existing system into which it will be integrated. The study’s findings, derived from both quantitative analysis and expert interviews, provide valuable contributions, including identified session clusters, their interpretation and indicators on which users might benefit from a CRS on these platforms.
Pachinger, Pia; Goldzycher, Janis; Planitzer, Anna; Kusa, Wojciech; Hanbury, Allan; Neidhardt, Julia
ÄustroTox: A Dataset for Target-Based Austrian German Offensive Language Detection” Proceedings Article
In: Ku, Lun-Wei; Martins, Andre; Srikumar, Vivek (Ed.): Findings of the Association for Computational Linguistics: ACL 2024, pp. 11990–12001, Association for Computational Linguistics, Bangkok, Thailand, 2024.
@inproceedings{pachinger-etal-2024-austrotox,
title = {ÄustroTox: A Dataset for Target-Based Austrian German Offensive Language Detection"},
author = {Pia Pachinger and Janis Goldzycher and Anna Planitzer and Wojciech Kusa and Allan Hanbury and Julia Neidhardt},
editor = {Lun-Wei Ku and Andre Martins and Vivek Srikumar},
url = {https://aclanthology.org/2024.findings-acl.713/},
doi = {10.18653/v1/2024.findings-acl.713},
year = {2024},
date = {2024-08-01},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2024},
pages = {11990–12001},
publisher = {Association for Computational Linguistics},
address = {Bangkok, Thailand},
abstract = {Model interpretability in toxicity detection greatly profits from token-level annotations. However, currently, such annotations are only available in English. We introduce a dataset annotated for offensive language detection sourced from a news forum, notable for its incorporation of the Austrian German dialect, comprising 4,562 user comments. In addition to binary offensiveness classification, we identify spans within each comment constituting vulgar language or representing targets of offensive statements. We evaluate fine-tuned Transformer models as well as large language models in a zero- and few-shot fashion. The results indicate that while fine-tuned models excel in detecting linguistic peculiarities such as vulgar dialect, large language models demonstrate superior performance in detecting offensiveness in AustroTox.},
keywords = {},
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Model interpretability in toxicity detection greatly profits from token-level annotations. However, currently, such annotations are only available in English. We introduce a dataset annotated for offensive language detection sourced from a news forum, notable for its incorporation of the Austrian German dialect, comprising 4,562 user comments. In addition to binary offensiveness classification, we identify spans within each comment constituting vulgar language or representing targets of offensive statements. We evaluate fine-tuned Transformer models as well as large language models in a zero- and few-shot fashion. The results indicate that while fine-tuned models excel in detecting linguistic peculiarities such as vulgar dialect, large language models demonstrate superior performance in detecting offensiveness in AustroTox.
Wagne, Ahmadou; Neidhardt, Julia; Kolb, Thomas Elmar
PopAut: An Annotated Corpus for Populism Detection in Austrian News Comments Proceedings Article
In: Calzolari, Nicoletta; Kan, Min-Yen; Hoste, Veronique; Lenci, Alessandro; Sakti, Sakriani; Xue, Nianwen (Ed.): Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp. 12879–12892, ELRA and ICCL, Torino, Italy, 2024.
@inproceedings{Wagne_Neidhardt_Kolb_2024,
title = {PopAut: An Annotated Corpus for Populism Detection in Austrian News Comments},
author = {Ahmadou Wagne and Julia Neidhardt and Thomas Elmar Kolb},
editor = {Nicoletta Calzolari and Min-Yen Kan and Veronique Hoste and Alessandro Lenci and Sakriani Sakti and Nianwen Xue},
url = {https://aclanthology.org/2024.lrec-main.1128},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
pages = {12879–12892},
publisher = {ELRA and ICCL},
address = {Torino, Italy},
abstract = {Populism is a phenomenon that is noticeably present in the political landscape of various countries over the past decades. While populism expressed by politicians has been thoroughly examined in the literature, populism expressed by citizens is still underresearched, especially when it comes to its automated detection in text. This work presents the PopAut corpus, which is the first annotated corpus of news comments for populism in the German language. It features 1,200 comments collected between 2019-2021 that are annotated for populist motives anti-elitism, people-centrism and people-sovereignty. Following the definition of Cas Mudde, populism is seen as a thin ideology. This work shows that annotators reach a high agreement when labeling news comments for these motives. The data set is collected to serve as the basis for automated populism detection using machine-learning methods. By using transformer-based models, we can outperform existing dictionaries tailored for automated populism detection in German social media content. Therefore our work provides a rich resource for future work on the classification of populist user comments in the German language.},
keywords = {},
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Populism is a phenomenon that is noticeably present in the political landscape of various countries over the past decades. While populism expressed by politicians has been thoroughly examined in the literature, populism expressed by citizens is still underresearched, especially when it comes to its automated detection in text. This work presents the PopAut corpus, which is the first annotated corpus of news comments for populism in the German language. It features 1,200 comments collected between 2019-2021 that are annotated for populist motives anti-elitism, people-centrism and people-sovereignty. Following the definition of Cas Mudde, populism is seen as a thin ideology. This work shows that annotators reach a high agreement when labeling news comments for these motives. The data set is collected to serve as the basis for automated populism detection using machine-learning methods. By using transformer-based models, we can outperform existing dictionaries tailored for automated populism detection in German social media content. Therefore our work provides a rich resource for future work on the classification of populist user comments in the German language.
Godolja, Dante; Kolb, Thomas Elmar; Neidhardt, Julia
Unlocking the Potential of Content-Based Restaurant Recommender Systems Proceedings Article
In: Berezina, Katerina; Nixon, Lyndon; Tuomi, Aarni (Ed.): Information and Communication Technologies in Tourism 2024, pp. 239–244, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-58839-6.
@inproceedings{10.1007/978-3-031-58839-6_26,
title = {Unlocking the Potential of Content-Based Restaurant Recommender Systems},
author = {Dante Godolja and Thomas Elmar Kolb and Julia Neidhardt},
editor = {Katerina Berezina and Lyndon Nixon and Aarni Tuomi},
isbn = {978-3-031-58839-6},
year = {2024},
date = {2024-01-01},
booktitle = {Information and Communication Technologies in Tourism 2024},
pages = {239–244},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Content-based restaurant recommender systems use features such as cuisine type, price range, and location to suggest dining options to users. Current research explores ways to improve their effectiveness. In this work, we explore different ideas on how to build a recommender system. We explore TF-IDF as a baseline and the state-of-the-art model SBERT. These ideas are tested on a real-world data-set of a digital restaurant guide. Evaluation involves both qualitative assessment by a domain expert and quantitative analysis. The results show that, with proper preprocessing, TF-IDF can achieve similar scores to SBERT and, depending on the scenario, even better results. However, SBERT still provides more novel recommendations than TF-IDF. Depending on the scenario, both models can be used to generate meaningful restaurant recommendations. However, more implicit aspects like a restaurant's atmosphere can hardly be captured by these models.},
keywords = {},
pubstate = {published},
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Content-based restaurant recommender systems use features such as cuisine type, price range, and location to suggest dining options to users. Current research explores ways to improve their effectiveness. In this work, we explore different ideas on how to build a recommender system. We explore TF-IDF as a baseline and the state-of-the-art model SBERT. These ideas are tested on a real-world data-set of a digital restaurant guide. Evaluation involves both qualitative assessment by a domain expert and quantitative analysis. The results show that, with proper preprocessing, TF-IDF can achieve similar scores to SBERT and, depending on the scenario, even better results. However, SBERT still provides more novel recommendations than TF-IDF. Depending on the scenario, both models can be used to generate meaningful restaurant recommendations. However, more implicit aspects like a restaurant’s atmosphere can hardly be captured by these models.
Scholz, Felix; Kolb, Thomas Elmar; Neidhardt, Julia
Classifying User Roles in Online News Forums: A Model for User Interaction and Behavior Analysis Proceedings Article
In: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, pp. 240–249, Association for Computing Machinery, Cagliari, Italy, 2024, ISBN: 9798400704666.
@inproceedings{10.1145/3631700.3665187,
title = {Classifying User Roles in Online News Forums: A Model for User Interaction and Behavior Analysis},
author = {Felix Scholz and Thomas Elmar Kolb and Julia Neidhardt},
url = {https://doi.org/10.1145/3631700.3665187},
doi = {10.1145/3631700.3665187},
isbn = {9798400704666},
year = {2024},
date = {2024-01-01},
booktitle = {Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {240–249},
publisher = {Association for Computing Machinery},
address = {Cagliari, Italy},
series = {UMAP Adjunct '24},
abstract = {The growing exchange of opinions in online news forums brings together a diverse cross-section of users with varying opinions and motivations. Understanding these behaviors is crucial for unraveling the composition of these large user bases. This study proposes an explainable model aimed at classifying users based on their activity and interaction patterns in online news forums. The model leverages exploratory and statistical data analysis to reveal recurring behaviors and provides a tool to analyze the evolution of large user communities, offering an overview of their composition. The model identifies six active roles: Taciturn, Silent Voter, Regular, Conversationalist, Power User, and Celebrity, and one inactive role, Lurker. The model was evaluated for its predictive power, achieving a macro F1 score of 0.8632, demonstrating its robustness. By applying the model to a long-term dataset from the online news forum derStandard.at, an analysis of role distribution over time was conducted. The results indicated a gradual increase in user activity within the forum. Moreover, the study assessed the co-occurrence of roles in users’ long-term behavior and measured the frequency of role changes. This analysis aimed to determine whether users have consistent roles or exhibit various roles, which may depend on time or context.},
keywords = {},
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The growing exchange of opinions in online news forums brings together a diverse cross-section of users with varying opinions and motivations. Understanding these behaviors is crucial for unraveling the composition of these large user bases. This study proposes an explainable model aimed at classifying users based on their activity and interaction patterns in online news forums. The model leverages exploratory and statistical data analysis to reveal recurring behaviors and provides a tool to analyze the evolution of large user communities, offering an overview of their composition. The model identifies six active roles: Taciturn, Silent Voter, Regular, Conversationalist, Power User, and Celebrity, and one inactive role, Lurker. The model was evaluated for its predictive power, achieving a macro F1 score of 0.8632, demonstrating its robustness. By applying the model to a long-term dataset from the online news forum derStandard.at, an analysis of role distribution over time was conducted. The results indicated a gradual increase in user activity within the forum. Moreover, the study assessed the co-occurrence of roles in users’ long-term behavior and measured the frequency of role changes. This analysis aimed to determine whether users have consistent roles or exhibit various roles, which may depend on time or context.
Nalis, Irina; Sippl, Tobias; Kolb, Thomas Elmar; Neidhardt, Julia
Navigating Serendipity – An Experimental User Study On The Interplay of Trust and Serendipity In Recommender Systems Proceedings Article
In: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, pp. 386–393, Association for Computing Machinery, Cagliari, Italy, 2024, ISBN: 9798400704666.
@inproceedings{10.1145/3631700.3664901,
title = {Navigating Serendipity - An Experimental User Study On The Interplay of Trust and Serendipity In Recommender Systems},
author = {Irina Nalis and Tobias Sippl and Thomas Elmar Kolb and Julia Neidhardt},
url = {https://doi.org/10.1145/3631700.3664901},
doi = {10.1145/3631700.3664901},
isbn = {9798400704666},
year = {2024},
date = {2024-01-01},
booktitle = {Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {386–393},
publisher = {Association for Computing Machinery},
address = {Cagliari, Italy},
series = {UMAP Adjunct '24},
abstract = {Recommender systems play a crucial role in our daily lives, constantly evolving to meet the diverse needs of users. As the pursuit of improved user experiences continues, metrics such as serendipity have emerged within the realm of beyond-accuracy paradigms. However, integrating serendipitous recommendations presents complex challenges, necessitating a delicate balance between novelty, relevance, and user engagement. In this interdisciplinary experimental user study, we address these challenges within the context of a book recommender system. By investigating the impact of interface design changes on user trust, a key determinant of satisfaction with serendipitous recommendations, we measured trust levels for both individual recommended items and the recommender system as a whole. Our findings indicate that while interface enhancements did not yield significant increases in trust, they did notably elevate serendipity ratings for previously unknown books. These results highlight the intricate interplay between technical and psychological factors in the design of recommender systems, emphasizing the importance of human-centered approaches in the creation of more responsible AI applications. This research contributes to ongoing discussions surrounding user-centric recommendation systems and aligns with broader themes of digital humanism and responsible AI.},
keywords = {},
pubstate = {published},
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}
Recommender systems play a crucial role in our daily lives, constantly evolving to meet the diverse needs of users. As the pursuit of improved user experiences continues, metrics such as serendipity have emerged within the realm of beyond-accuracy paradigms. However, integrating serendipitous recommendations presents complex challenges, necessitating a delicate balance between novelty, relevance, and user engagement. In this interdisciplinary experimental user study, we address these challenges within the context of a book recommender system. By investigating the impact of interface design changes on user trust, a key determinant of satisfaction with serendipitous recommendations, we measured trust levels for both individual recommended items and the recommender system as a whole. Our findings indicate that while interface enhancements did not yield significant increases in trust, they did notably elevate serendipity ratings for previously unknown books. These results highlight the intricate interplay between technical and psychological factors in the design of recommender systems, emphasizing the importance of human-centered approaches in the creation of more responsible AI applications. This research contributes to ongoing discussions surrounding user-centric recommendation systems and aligns with broader themes of digital humanism and responsible AI.
Huebner, Blake; Kolb, Thomas Elmar; Neidhardt, Julia
Evaluating Group Fairness in News Recommendations: A Comparative Study of Algorithms and Metrics Proceedings Article
In: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, pp. 337–346, Association for Computing Machinery, Cagliari, Italy, 2024, ISBN: 9798400704666.
@inproceedings{10.1145/3631700.3664897,
title = {Evaluating Group Fairness in News Recommendations: A Comparative Study of Algorithms and Metrics},
author = {Blake Huebner and Thomas Elmar Kolb and Julia Neidhardt},
url = {https://doi.org/10.1145/3631700.3664897},
doi = {10.1145/3631700.3664897},
isbn = {9798400704666},
year = {2024},
date = {2024-01-01},
booktitle = {Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {337–346},
publisher = {Association for Computing Machinery},
address = {Cagliari, Italy},
series = {UMAP Adjunct '24},
abstract = {Beyond accuracy metrics, such as fairness and diversity, have become widely studied topics in recommender systems. Improving these metrics is important not only from an ethical and legal perspective, but can also improve overall user satisfaction. Although these metrics are widely discussed, very little empirical research has been done, especially comparing multiple algorithms across different metrics. This work explores the role of fairness and diversity in news recommender systems, specifically in the context of the Austrian media landscape. This study aims to identify the most effective approaches for generating fair and diverse news recommendations, while addressing the potential negative consequences of biased recommendations and filter bubbles, such as societal polarization and the suppression of information. This includes an extensive literature review of relevant group unfairness metrics and state-of-the-art fairness-aware algorithms. A dataset of articles from an Austrian newspaper was used for empirical research, with analysis performed on fairness, and diversity of recommendations. The key message of the study is that accuracy and fairness can be achieved simultaneously with the right modeling approach, while diversity can be held constant using these modeling techniques. The study recommends the use of Personalized Fairness based on Causal Notion models for accuracy and reducing certain unfairness metrics, and finds Fairness Objectives for Collaborative Filtering models more effective at reducing other types of unfairness. The findings contribute to the field by demonstrating the importance of incorporating these metrics into the design and evaluation of recommender systems.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Beyond accuracy metrics, such as fairness and diversity, have become widely studied topics in recommender systems. Improving these metrics is important not only from an ethical and legal perspective, but can also improve overall user satisfaction. Although these metrics are widely discussed, very little empirical research has been done, especially comparing multiple algorithms across different metrics. This work explores the role of fairness and diversity in news recommender systems, specifically in the context of the Austrian media landscape. This study aims to identify the most effective approaches for generating fair and diverse news recommendations, while addressing the potential negative consequences of biased recommendations and filter bubbles, such as societal polarization and the suppression of information. This includes an extensive literature review of relevant group unfairness metrics and state-of-the-art fairness-aware algorithms. A dataset of articles from an Austrian newspaper was used for empirical research, with analysis performed on fairness, and diversity of recommendations. The key message of the study is that accuracy and fairness can be achieved simultaneously with the right modeling approach, while diversity can be held constant using these modeling techniques. The study recommends the use of Personalized Fairness based on Causal Notion models for accuracy and reducing certain unfairness metrics, and finds Fairness Objectives for Collaborative Filtering models more effective at reducing other types of unfairness. The findings contribute to the field by demonstrating the importance of incorporating these metrics into the design and evaluation of recommender systems.
Neidhardt, Julia
Transforming recommender systems: balancing personalization, fairness, and human values Proceedings Article
In: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, Jeju, Korea, 2024, ISBN: 978-1-956792-04-1.
@inproceedings{10.24963/ijcai.2024/982,
title = {Transforming recommender systems: balancing personalization, fairness, and human values},
author = {Julia Neidhardt},
url = {https://doi.org/10.24963/ijcai.2024/982},
doi = {10.24963/ijcai.2024/982},
isbn = {978-1-956792-04-1},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence},
address = {Jeju, Korea},
series = {IJCAI '24},
abstract = {Recent advancements in recommender systems highlight the importance of metrics beyond accuracy, including diversity, serendipity, and fairness. This paper discusses various aspects of modern recommender systems, focusing on challenges such as preference elicitation, the complexity of human decision-making, and multi-domain applicability. The integration of Generative AI and Large Language Models offers enhanced personalization capabilities but also raises concerns regarding transparency and fairness. This work examines ongoing research efforts aimed at developing transparent, fair, and contextually aware systems. Our approach seeks to prioritize user wellbeing and responsibility, contributing to a more equitable and functional digital environment through advanced technologies and interdisciplinary insights.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent advancements in recommender systems highlight the importance of metrics beyond accuracy, including diversity, serendipity, and fairness. This paper discusses various aspects of modern recommender systems, focusing on challenges such as preference elicitation, the complexity of human decision-making, and multi-domain applicability. The integration of Generative AI and Large Language Models offers enhanced personalization capabilities but also raises concerns regarding transparency and fairness. This work examines ongoing research efforts aimed at developing transparent, fair, and contextually aware systems. Our approach seeks to prioritize user wellbeing and responsibility, contributing to a more equitable and functional digital environment through advanced technologies and interdisciplinary insights.
Aayesha, Aayesha; Afzaal, Muhammad; Neidhardt, Julia
User Experience of Recommender System: A User Study of Social-aware Fashion Recommendations System Proceedings Article
In: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, pp. 356–361, Association for Computing Machinery, Cagliari, Italy, 2024, ISBN: 9798400704666.
@inproceedings{10.1145/3631700.3664896,
title = {User Experience of Recommender System: A User Study of Social-aware Fashion Recommendations System},
author = {Aayesha Aayesha and Muhammad Afzaal and Julia Neidhardt},
url = {https://doi.org/10.1145/3631700.3664896},
doi = {10.1145/3631700.3664896},
isbn = {9798400704666},
year = {2024},
date = {2024-01-01},
booktitle = {Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {356–361},
publisher = {Association for Computing Machinery},
address = {Cagliari, Italy},
series = {UMAP Adjunct '24},
abstract = {User experience, which encompasses users’ feelings and perceptions, is regarded as a key element in the evaluation of recommender systems. The existing literature extensively works on recommendation generation strategies with focus on the accuracy by considering objective aspects of the system. Although some of the current works considered subjective aspects of the recommendation systems from a user-centric perspective to evaluate the recommender system, however, a comprehensive analysis that could investigate factors to improve user experience was of limited focus. In this paper, we propose a methodology that provides a comprehensive multi-perspective analysis of a social-aware fashion recommender system and analyses the impact of user’s personal attributes and profiles on their experiences in various aspects of system use. A user study was conducted to realize the proposed methodology. The obtained insights highlighted that user experiences vary not only from the perspective of using a recommender system but also by varying their personal attributes (age, gender, hobby) and profiles.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
User experience, which encompasses users’ feelings and perceptions, is regarded as a key element in the evaluation of recommender systems. The existing literature extensively works on recommendation generation strategies with focus on the accuracy by considering objective aspects of the system. Although some of the current works considered subjective aspects of the recommendation systems from a user-centric perspective to evaluate the recommender system, however, a comprehensive analysis that could investigate factors to improve user experience was of limited focus. In this paper, we propose a methodology that provides a comprehensive multi-perspective analysis of a social-aware fashion recommender system and analyses the impact of user’s personal attributes and profiles on their experiences in various aspects of system use. A user study was conducted to realize the proposed methodology. The obtained insights highlighted that user experiences vary not only from the perspective of using a recommender system but also by varying their personal attributes (age, gender, hobby) and profiles.
Aayesha, Aayesha; Afzaal, Muhammad; Neidhardt, Julia
Social Circle-Enhanced Fashion Recommendations System Proceedings Article
In: Brusilovsky, Peter; Gemmis, Marco; Felfernig, Alexander (Ed.): pp. 81–91, 2024.
@inproceedings{ 20.500.12708_208019,
title = {Social Circle-Enhanced Fashion Recommendations System},
author = {Aayesha Aayesha and Muhammad Afzaal and Julia Neidhardt},
editor = {Peter Brusilovsky and Marco Gemmis and Alexander Felfernig},
year = {2024},
date = {2024-01-01},
volume = {3815},
pages = {81–91},
abstract = {When shopping for fashionable clothing items, consumers frequently experience indecision and struggle to make choices, resulting in a stalling of the purchasing process. In such scenarios, most often they need support of their friends from their social circle to choose suitable clothes for different events. To provide decision-making support, considerable research has focused on generating social-aware recommendations that incorporate input from the user’s social circle. However, there has been minimal research dedicated to develop and evaluate such systems that could assess the importance of social circles in producing social-aware fashion recommendations and identifying factors that might enhance these recommendations. This paper addresses these limitations by developing a Social Circle-Enhanced Fashion Recommendation (SCEFR) System that encompasses friends feedback to generate recommendations. The SCEFR system was evaluated by conducting a user study, comparing system-generated recommendations with user choices as rank correlation coefficients. The findings indicate that inputs from the social circle alone have limited potential in generating effective social-aware recommendations. However, when the user’s shopping preferences were shared with their social circle, the quality of these recommendations significantly improved, as evidenced by a qualitative analysis of user feedback. Furthermore, in comparative analysis with the state-of-the-art (SOTA) approaches of recommendation generation, the SCEFR system informed by user’s shopping preferences demonstrated superiority.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
When shopping for fashionable clothing items, consumers frequently experience indecision and struggle to make choices, resulting in a stalling of the purchasing process. In such scenarios, most often they need support of their friends from their social circle to choose suitable clothes for different events. To provide decision-making support, considerable research has focused on generating social-aware recommendations that incorporate input from the user’s social circle. However, there has been minimal research dedicated to develop and evaluate such systems that could assess the importance of social circles in producing social-aware fashion recommendations and identifying factors that might enhance these recommendations. This paper addresses these limitations by developing a Social Circle-Enhanced Fashion Recommendation (SCEFR) System that encompasses friends feedback to generate recommendations. The SCEFR system was evaluated by conducting a user study, comparing system-generated recommendations with user choices as rank correlation coefficients. The findings indicate that inputs from the social circle alone have limited potential in generating effective social-aware recommendations. However, when the user’s shopping preferences were shared with their social circle, the quality of these recommendations significantly improved, as evidenced by a qualitative analysis of user feedback. Furthermore, in comparative analysis with the state-of-the-art (SOTA) approaches of recommendation generation, the SCEFR system informed by user’s shopping preferences demonstrated superiority.
Neidhardt, Julia; Kuflik, Tsvi; Livne, Amit; Zanker, Markus
Workshop on Recommenders in Tourism (RecTour) 2024 Proceedings Article
In: Proceedings of the 18th ACM Conference on Recommender Systems, pp. 1229–1231, Association for Computing Machinery, Bari, Italy, 2024, ISBN: 9798400705052.
@inproceedings{10.1145/3640457.3687107,
title = {Workshop on Recommenders in Tourism (RecTour) 2024},
author = {Julia Neidhardt and Tsvi Kuflik and Amit Livne and Markus Zanker},
url = {https://doi.org/10.1145/3640457.3687107},
doi = {10.1145/3640457.3687107},
isbn = {9798400705052},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the 18th ACM Conference on Recommender Systems},
pages = {1229–1231},
publisher = {Association for Computing Machinery},
address = {Bari, Italy},
series = {RecSys '24},
abstract = {The Workshop on Recommenders in Tourism (RecTour) has been successfully held in conjunction with the ACM Conference on Recommender Systems (RecSys) since 2016, with the exception of one year. This workshop focuses on the unique and evolving challenges of recommender systems in the tourism domain. Over time, RecTour has fostered an active community supported by both academia and industry. This year, the workshop features a special challenge focused on ranking travel reviews. In this overview paper, we outline our motivations for organizing the RecTour workshop and highlight the main topics covered in RecTour submissions, including destination recommendation, privacy concerns in travel recommender systems, the cold-start problem, transformer-based approaches in recommendation systems, and best practices for evaluation and experimentation.},
keywords = {},
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}
The Workshop on Recommenders in Tourism (RecTour) has been successfully held in conjunction with the ACM Conference on Recommender Systems (RecSys) since 2016, with the exception of one year. This workshop focuses on the unique and evolving challenges of recommender systems in the tourism domain. Over time, RecTour has fostered an active community supported by both academia and industry. This year, the workshop features a special challenge focused on ranking travel reviews. In this overview paper, we outline our motivations for organizing the RecTour workshop and highlight the main topics covered in RecTour submissions, including destination recommendation, privacy concerns in travel recommender systems, the cold-start problem, transformer-based approaches in recommendation systems, and best practices for evaluation and experimentation.
Nalis, Irina; Neidhardt, Julia
Towards Possibility: Interdisciplinary Perspectives on Enhancing Recommender Systems Beyond Accuracy Miscellaneous
2024.
@misc{ 20.500.12708_210789,
title = {Towards Possibility: Interdisciplinary Perspectives on Enhancing Recommender Systems Beyond Accuracy},
author = {Irina Nalis and Julia Neidhardt},
year = {2024},
date = {2024-01-01},
abstract = {In this introductory lecture, Dr. Julia Neidhardt, Director of the CDLab for Recommender Systems and UNESCO Co-Chair in Digital Humanism, and Dr. Irina Nalis, psychologist, and interdisciplinary researcher at the CDLab, explore recommender systems from a Digital Humanism viewpoint, focusing on the intersection of technology, psychology, and societal needs. Addressing the limitations and risks of accuracy-centric metrics, they emphasize the importance of establishing new research and development methods to go beyond accuracy and towards more human-potential centered recommendations. Their lecture, based on interdisciplinary research including advanced algorithms, cross-domain recommendations, and the integration of large-language models, demonstrates the potential of recommender systems to foster diversity, serendipity, and democratic fairness. They further discuss the role of choice architecture and affordances in making responsible recommendations, highlighting the importance of aligning with broader policies like the EU Digital Services Act to meet societal needs and enrich the digital landscape.},
keywords = {},
pubstate = {published},
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}
In this introductory lecture, Dr. Julia Neidhardt, Director of the CDLab for Recommender Systems and UNESCO Co-Chair in Digital Humanism, and Dr. Irina Nalis, psychologist, and interdisciplinary researcher at the CDLab, explore recommender systems from a Digital Humanism viewpoint, focusing on the intersection of technology, psychology, and societal needs. Addressing the limitations and risks of accuracy-centric metrics, they emphasize the importance of establishing new research and development methods to go beyond accuracy and towards more human-potential centered recommendations. Their lecture, based on interdisciplinary research including advanced algorithms, cross-domain recommendations, and the integration of large-language models, demonstrates the potential of recommender systems to foster diversity, serendipity, and democratic fairness. They further discuss the role of choice architecture and affordances in making responsible recommendations, highlighting the importance of aligning with broader policies like the EU Digital Services Act to meet societal needs and enrich the digital landscape.
Pachinger, Pia; Goldzycher, Janis; Planitzer, Anna Maria; Kusa, Wojciech; Hanbury, Allan; Neidhardt, Julia
A Dataset for Span-Based Austrian German and English Offensive Language Detection Miscellaneous
2024.
@misc{ 20.500.12708_210352,
title = {A Dataset for Span-Based Austrian German and English Offensive Language Detection},
author = {Pia Pachinger and Janis Goldzycher and Anna Maria Planitzer and Wojciech Kusa and Allan Hanbury and Julia Neidhardt},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
2023
Kolb, Thomas Elmar; Nalis-Neuner, Irina; Neidhardt, Julia
Like a Skilled DJ – an Expert Study on News Recommendations Beyond Accuracy Proceedings Article
In: Kille, Benjamin (Ed.): CEUR-WS.org, 2023.
@inproceedings{ 20.500.12708_191170,
title = {Like a Skilled DJ - an Expert Study on News Recommendations Beyond Accuracy},
author = {Thomas Elmar Kolb and Irina Nalis-Neuner and Julia Neidhardt},
editor = {Benjamin Kille},
doi = {10.34726/5332},
year = {2023},
date = {2023-01-01},
volume = {3561},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {In the past, recommender systems were primarily focused on optimizing accuracy. However, in recent years, there has been an increasing awareness that considerations beyond accuracy are necessary. The definition of what constitutes a good recommendation is a crucial issue. The most precise prediction may not always be the recommendation that satisfies the user best. This study offers a comprehensive investigation into the present advancements within the realm of beyond-accuracy measurements, especially the metrics diversity, serendipity, and novelty. Collaborative efforts between algorithmic models and domain experts can enrich recommendation quality, particularly in labeling and categorizing content. To address this, we present a study conducted by experts in the news domain. This study provides new insights into the multifaceted nature of this challenge. Employing an interdisciplinary approach, we underscore the significance of constructing a system that revolves around the user. Recent discussions about algorithmic content filtering and its societal implications underscore the importance of maintaining human involvement in the decision-making loop.},
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In the past, recommender systems were primarily focused on optimizing accuracy. However, in recent years, there has been an increasing awareness that considerations beyond accuracy are necessary. The definition of what constitutes a good recommendation is a crucial issue. The most precise prediction may not always be the recommendation that satisfies the user best. This study offers a comprehensive investigation into the present advancements within the realm of beyond-accuracy measurements, especially the metrics diversity, serendipity, and novelty. Collaborative efforts between algorithmic models and domain experts can enrich recommendation quality, particularly in labeling and categorizing content. To address this, we present a study conducted by experts in the news domain. This study provides new insights into the multifaceted nature of this challenge. Employing an interdisciplinary approach, we underscore the significance of constructing a system that revolves around the user. Recent discussions about algorithmic content filtering and its societal implications underscore the importance of maintaining human involvement in the decision-making loop.
Kolb, Thomas Elmar; Wagne, Ahmadou; Sertkan, Mete; Neidhardt, Julia
Potentials of Combining Local Knowledge and LLMs for Recommender Systems Proceedings Article
In: Anelli, Vito Walter; Basile, Pierpaolo; Melo, Gerard De; Donini, Francesco; Ferrara, Antonio; Musto, Cataldo; Narducci, Fedelucio; Ragone, Azzurra; Zanker, Markus (Ed.): pp. 61–64, CEUR-WS.org, 2023.
@inproceedings{ 20.500.12708_191183,
title = {Potentials of Combining Local Knowledge and LLMs for Recommender Systems},
author = {Thomas Elmar Kolb and Ahmadou Wagne and Mete Sertkan and Julia Neidhardt},
editor = {Vito Walter Anelli and Pierpaolo Basile and Gerard De Melo and Francesco Donini and Antonio Ferrara and Cataldo Musto and Fedelucio Narducci and Azzurra Ragone and Markus Zanker},
doi = {10.34726/5334},
year = {2023},
date = {2023-01-01},
volume = {3560},
pages = {61–64},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {LLMs have revolutionized the understanding and generation of natural language, offering new possibilities for enhancing recommendation systems. In previous studies, LLMs exploit their global knowledge to provide zero- or few-shot recommendations. In this work, we aim to highlight the opportunities that LLMs pose to enrich the field of recommender systems combined with local knowledge. We propose to view recommender systems combined with LLMs from a broader perspective, recognizing them not merely as another method to replace existing recommendation approaches, but rather as a complementary and powerful approach to enhance and augment the overall recommendation process.},
keywords = {},
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LLMs have revolutionized the understanding and generation of natural language, offering new possibilities for enhancing recommendation systems. In previous studies, LLMs exploit their global knowledge to provide zero- or few-shot recommendations. In this work, we aim to highlight the opportunities that LLMs pose to enrich the field of recommender systems combined with local knowledge. We propose to view recommender systems combined with LLMs from a broader perspective, recognizing them not merely as another method to replace existing recommendation approaches, but rather as a complementary and powerful approach to enhance and augment the overall recommendation process.
Sertkan, Mete; Althammer, Sophia; Hofstätter, Sebastian; Knees, Peter; Neidhardt, Julia
Exploring Effect-Size-Based Meta-Analysis for Multi-Dataset Evaluation Proceedings Article
In: CEUR-WS.org, 2023.
@inproceedings{ 20.500.12708_191697,
title = {Exploring Effect-Size-Based Meta-Analysis for Multi-Dataset Evaluation},
author = {Mete Sertkan and Sophia Althammer and Sebastian Hofstätter and Peter Knees and Julia Neidhardt},
doi = {10.34726/5352},
year = {2023},
date = {2023-01-01},
volume = {3476},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {In this paper, we address the essential yet complex task of evaluating Recommender Systems (RecSys) across multiple datasets. This is critical for gauging their overall performance and applicability in various contexts. Owing to the unique characteristics of each dataset and the variability in algorithm performance, we propose the adoption of effect-size-based meta-analysis, a proven tool in comparative research. This approach enables us to compare a “treatment model” and a “control model” across multiple datasets, offering a comprehensive evaluation of their performance. Through two case studies, we highlight the flexibility and effectiveness of this method in multi-dataset evaluations, irrespective of the metric utilized. The power of forest plots in providing an intuitive and concise summarization of our analysis is also demonstrated, which significantly aids in the communication of research findings. Our work provides valuable insights into leveraging these methodologies to draw more reliable and validated conclusions on the generalizability and robustness of RecSys models.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we address the essential yet complex task of evaluating Recommender Systems (RecSys) across multiple datasets. This is critical for gauging their overall performance and applicability in various contexts. Owing to the unique characteristics of each dataset and the variability in algorithm performance, we propose the adoption of effect-size-based meta-analysis, a proven tool in comparative research. This approach enables us to compare a “treatment model” and a “control model” across multiple datasets, offering a comprehensive evaluation of their performance. Through two case studies, we highlight the flexibility and effectiveness of this method in multi-dataset evaluations, irrespective of the metric utilized. The power of forest plots in providing an intuitive and concise summarization of our analysis is also demonstrated, which significantly aids in the communication of research findings. Our work provides valuable insights into leveraging these methodologies to draw more reliable and validated conclusions on the generalizability and robustness of RecSys models.
Basso, Linda; Nalis-Neuner, Irina; Neidhardt, Julia
News Diversity and Well-Being – An Experimental Exploration Of Diversity-Aware Recommender Systems Proceedings Article
In: 2023.
@inproceedings{ 20.500.12708_193212,
title = {News Diversity and Well-Being – An Experimental Exploration Of Diversity-Aware Recommender Systems},
author = {Linda Basso and Irina Nalis-Neuner and Julia Neidhardt},
year = {2023},
date = {2023-01-01},
abstract = {The demand for socially responsible designs for news recommender systems is currently of the utmost relevance. This paper presents a novel and interdisciplinary approach, bringing together psychol- ogists and computer scientists, to examine the impact of diverse news recommendations on individual users. In this experimental study, participants were divided into two groups, interacting with either a diverse news recommender system (experimental group) or a non-diversified system (control group). Subjective well-being and personal evaluations of the recommender system were measured. Although the study did not find a significant positive impact on participants’ subjective well-being after consuming more diverse news, this preliminary investigation opens avenues for further re- search. This study sets the stage for future investigations, providing valuable insights and highlighting the complexities of promoting diverse news consumption through recommender systems. Further research is warranted to explore potential enhancements and refine the understanding of the relationship between diversified news recommendations and user well-being. This contribution lays a groundstone for further research on responsibilities and how to implement basic human values, which are important to sustain and advance the democratic society we live in.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The demand for socially responsible designs for news recommender systems is currently of the utmost relevance. This paper presents a novel and interdisciplinary approach, bringing together psychol- ogists and computer scientists, to examine the impact of diverse news recommendations on individual users. In this experimental study, participants were divided into two groups, interacting with either a diverse news recommender system (experimental group) or a non-diversified system (control group). Subjective well-being and personal evaluations of the recommender system were measured. Although the study did not find a significant positive impact on participants’ subjective well-being after consuming more diverse news, this preliminary investigation opens avenues for further re- search. This study sets the stage for future investigations, providing valuable insights and highlighting the complexities of promoting diverse news consumption through recommender systems. Further research is warranted to explore potential enhancements and refine the understanding of the relationship between diversified news recommendations and user well-being. This contribution lays a groundstone for further research on responsibilities and how to implement basic human values, which are important to sustain and advance the democratic society we live in.
Neidhardt, Julia; Wörndl, Wolfgang; Kuflik, Tsvi; Goldenberg, Dmitri; Zanker, Markus
Workshop on Recommenders in Tourism (RecTour) 2023 Proceedings Article
In: Zhang, Jie; Chen, Li; Berkovsky, Shlomo (Ed.): pp. 1274–1275, Association for Computing Machinery, New York, 2023.
@inproceedings{ 20.500.12708_191202,
title = {Workshop on Recommenders in Tourism (RecTour) 2023},
author = {Julia Neidhardt and Wolfgang Wörndl and Tsvi Kuflik and Dmitri Goldenberg and Markus Zanker},
editor = {Jie Zhang and Li Chen and Shlomo Berkovsky},
doi = {10.1145/3604915.3608764},
year = {2023},
date = {2023-01-01},
pages = {1274–1275},
publisher = {Association for Computing Machinery},
address = {New York},
abstract = {The Workshop on Recommenders in Tourism (RecTour) 2023, which is held in conjunction with the 17th issue of the ACM Conference on Recommender Systems (RecSys) in Singapore, addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The Workshop on Recommenders in Tourism (RecTour) 2023, which is held in conjunction with the 17th issue of the ACM Conference on Recommender Systems (RecSys) in Singapore, addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.
Nalis, Irina; Neidhardt, Julia
Not Facial Expression, nor Fingerprint – Acknowledging Complexity and Context in Emotion Research for Human-Centered Personalization and Adaptation Proceedings Article
In: Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, pp. 325–330, Association for Computing Machinery, New York, 2023.
@inproceedings{ 20.500.12708_192197,
title = {Not Facial Expression, nor Fingerprint – Acknowledging Complexity and Context in Emotion Research for Human-Centered Personalization and Adaptation},
author = {Irina Nalis and Julia Neidhardt},
doi = {10.1145/3563359.3596990},
year = {2023},
date = {2023-01-01},
booktitle = {Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization},
pages = {325–330},
publisher = {Association for Computing Machinery},
address = {New York},
abstract = {While research on emotion has emerged as a crucial area in studying this relationship, the use of classical psychological concepts in human emotion detection and sentiment analysis has been challenged by the cognitive sciences and psychology. This paper argues that the uncritical adoption of concepts that overlook the complexity and context of emotions may hinder progress in this field. To overcome this limitation, the theory of constructed emotion is reviewed, which suggests that emotions are not distinct categories but rather dimensions that require dynamic, rather than static, contextualized models. By prioritizing digital wellbeing in emotion studies and acknowledging complexity and context, future research can develop more effective models for emotion detection and sentiment analysis. The aim is to provide valuable insights for researchers seeking to advance our understanding of the relationship between technology and wellbeing for human centered-adaptation and personalization.},
keywords = {},
pubstate = {published},
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While research on emotion has emerged as a crucial area in studying this relationship, the use of classical psychological concepts in human emotion detection and sentiment analysis has been challenged by the cognitive sciences and psychology. This paper argues that the uncritical adoption of concepts that overlook the complexity and context of emotions may hinder progress in this field. To overcome this limitation, the theory of constructed emotion is reviewed, which suggests that emotions are not distinct categories but rather dimensions that require dynamic, rather than static, contextualized models. By prioritizing digital wellbeing in emotion studies and acknowledging complexity and context, future research can develop more effective models for emotion detection and sentiment analysis. The aim is to provide valuable insights for researchers seeking to advance our understanding of the relationship between technology and wellbeing for human centered-adaptation and personalization.
Knees, Peter; Neidhardt, Julia; Nalis-Neuner, Irina
Recommender Systems: Techniques, Effects, and Measures Toward Pluralism and Fairness Book Section
In: Werthner, Hannes; Ghezzi, Carlo; Kramer, Jeff (Ed.): pp. 417–434, Springer, Cham, 2023.
@incollection{ 20.500.12708_191188,
title = {Recommender Systems: Techniques, Effects, and Measures Toward Pluralism and Fairness},
author = {Peter Knees and Julia Neidhardt and Irina Nalis-Neuner},
editor = {Hannes Werthner and Carlo Ghezzi and Jeff Kramer},
doi = {10.1007/978-3-031-45304-5_27},
year = {2023},
date = {2023-01-01},
pages = {417–434},
publisher = {Springer},
address = {Cham},
abstract = {Recommender systems are widely used in various applications, such as online shopping, social media, and news personalization. They can help systems by delivering only the most relevant and promising information to their users and help people by mitigating information overload. At the same time, algorithmic recommender systems are a new form of gatekeeper that preselects and controls the information being presented and actively shapes users’ choices and behavior. This becomes a crucial aspect, as, if unaddressed and not safeguarded, these systems are susceptible to perpetuate and even amplify existing biases, including unwanted societal biases, leading to unfair and discriminatory outcomes. In this chapter, we briefly introduce recommender systems, their basic mechanisms, and their importance in various applications. We show how their outcomes and performance are assessed and discuss approaches to addressing pluralism and fairness in recommender systems. Finally, we highlight recently emerging directions within recommender systems research, pointing out opportunities for digital humanism to contribute interdisciplinary expertise.},
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Recommender systems are widely used in various applications, such as online shopping, social media, and news personalization. They can help systems by delivering only the most relevant and promising information to their users and help people by mitigating information overload. At the same time, algorithmic recommender systems are a new form of gatekeeper that preselects and controls the information being presented and actively shapes users’ choices and behavior. This becomes a crucial aspect, as, if unaddressed and not safeguarded, these systems are susceptible to perpetuate and even amplify existing biases, including unwanted societal biases, leading to unfair and discriminatory outcomes. In this chapter, we briefly introduce recommender systems, their basic mechanisms, and their importance in various applications. We show how their outcomes and performance are assessed and discuss approaches to addressing pluralism and fairness in recommender systems. Finally, we highlight recently emerging directions within recommender systems research, pointing out opportunities for digital humanism to contribute interdisciplinary expertise.
Grossmann, Wilfried; Sertkan, Mete; Neidhardt, Julia; Werthner, Hannes
Pictures as a tool for matching tourist preferences with destinations Book Section
In: Augstein, Mirjam; Herder, Eelco; Wörndl, Wolfgang (Ed.): pp. 337–354, De Gruyter Oldenbourg, Berlin ; Boston, 2023.
@incollection{ 20.500.12708_191182,
title = {Pictures as a tool for matching tourist preferences with destinations},
author = {Wilfried Grossmann and Mete Sertkan and Julia Neidhardt and Hannes Werthner},
editor = {Mirjam Augstein and Eelco Herder and Wolfgang Wörndl},
doi = {10.1515/9783110988567-013},
year = {2023},
date = {2023-01-01},
pages = {337–354},
publisher = {De Gruyter Oldenbourg},
address = {Berlin ; Boston},
series = {De Gruyter Textbook},
abstract = {Usually descriptions of touristic products comprise information about accommodation, tourist attractions or leisure activities. Tourist decisions for a product are based on personal characteristics, planned vacation activities and specificities of potential touristic products. The decision should guarantee a high level of emotional and physical well-being, considering also some hard constraints like temporal and monetary resources, or travel distance. The starting point for the design of the described recommender system is a unified description of the preferences of the tourist and the opportunities offered by touristic products using the so-called seven-factor model. For the assignment of the values in the seven-factor model a predefined set of pictures is the pivotal instrument. These pictures represent various aspects of the personality and preferences of the tourist as well as general categories for the description of destinations, i. e., certain tourist attractions like landscape, cultural facilities, different leisure activities or emotional aspects associated with tourism. Based on the picture selection of a customer a so-called factor algorithm calculates values for each factor of the seven-factor model. This is a rather fast and intuitive method for acquisition of information about personality and preferences. The evaluation of the factors of the products is obtained by mapping descriptive attributes of touristic products onto the predefined pictures and afterwards applying the factor algorithm to the pictures characterizing the product. Based on this unified description of tourists and touristic products a recommendation can be defined by measuring the similarity between the user attributes and the product attributes. The approach is evaluated using data from a travel agency. Furthermore, other possible applications are discussed.},
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Usually descriptions of touristic products comprise information about accommodation, tourist attractions or leisure activities. Tourist decisions for a product are based on personal characteristics, planned vacation activities and specificities of potential touristic products. The decision should guarantee a high level of emotional and physical well-being, considering also some hard constraints like temporal and monetary resources, or travel distance. The starting point for the design of the described recommender system is a unified description of the preferences of the tourist and the opportunities offered by touristic products using the so-called seven-factor model. For the assignment of the values in the seven-factor model a predefined set of pictures is the pivotal instrument. These pictures represent various aspects of the personality and preferences of the tourist as well as general categories for the description of destinations, i. e., certain tourist attractions like landscape, cultural facilities, different leisure activities or emotional aspects associated with tourism. Based on the picture selection of a customer a so-called factor algorithm calculates values for each factor of the seven-factor model. This is a rather fast and intuitive method for acquisition of information about personality and preferences. The evaluation of the factors of the products is obtained by mapping descriptive attributes of touristic products onto the predefined pictures and afterwards applying the factor algorithm to the pictures characterizing the product. Based on this unified description of tourists and touristic products a recommendation can be defined by measuring the similarity between the user attributes and the product attributes. The approach is evaluated using data from a travel agency. Furthermore, other possible applications are discussed.
Baumann, Andreas; Hofmann, Klaus; Marakasova, Anna; Neidhardt, Julia; Wissik, Tanja
Semantic micro-dynamics as a reflex of occurrence frequency: a semantic networks approach Journal Article
In: Cognitive Linguistics, vol. 34, iss. 3-4, pp. 533–568, 2023, ISSN: 0936-5907.
@article{ 20.500.12708_191531,
title = {Semantic micro-dynamics as a reflex of occurrence frequency: a semantic networks approach},
author = {Andreas Baumann and Klaus Hofmann and Anna Marakasova and Julia Neidhardt and Tanja Wissik},
url = {https://api.elsevier.com/content/abstract/scopus_id/85175416448},
doi = {10.1515/cog-2022-0008},
issn = {0936-5907},
year = {2023},
date = {2023-01-01},
journal = {Cognitive Linguistics},
volume = {34},
issue = {3-4},
pages = {533–568},
publisher = {De Gruyter},
abstract = {This article correlates fine-grained semantic variability and change with measures of occurrence frequency to investigate whether a word's degree of semantic change is sensitive to how often it is used. We show that this sensitivity can be detected within a short time span (i.e., 20 years), basing our analysis on a large corpus of German allowing for a high temporal resolution (i.e., per month). We measure semantic variability and change with the help of local semantic networks, combining elements of deep learning methodology and graph theory. Our micro-scale analysis complements previous macro-scale studies from the field of natural language processing, corroborating the finding that high token frequency has a negative effect on the degree of semantic change in a lexical item. We relate this relationship to the role of exemplars for establishing form-function pairings between words and their habitual usage contexts.},
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This article correlates fine-grained semantic variability and change with measures of occurrence frequency to investigate whether a word’s degree of semantic change is sensitive to how often it is used. We show that this sensitivity can be detected within a short time span (i.e., 20 years), basing our analysis on a large corpus of German allowing for a high temporal resolution (i.e., per month). We measure semantic variability and change with the help of local semantic networks, combining elements of deep learning methodology and graph theory. Our micro-scale analysis complements previous macro-scale studies from the field of natural language processing, corroborating the finding that high token frequency has a negative effect on the degree of semantic change in a lexical item. We relate this relationship to the role of exemplars for establishing form-function pairings between words and their habitual usage contexts.
Pachinger, Pia; Hanbury, Allan; Neidhardt, Julia; Planitzer, Anna Maria
Toward Disambiguating the Definitions of Abusive, Offensive, Toxic, and Uncivil Comments Proceedings Article
In: pp. 107–113, 2023.
@inproceedings{ 20.500.12708_191532,
title = {Toward Disambiguating the Definitions of Abusive, Offensive, Toxic, and Uncivil Comments},
author = {Pia Pachinger and Allan Hanbury and Julia Neidhardt and Anna Maria Planitzer},
doi = {10.18653/v1/2023.c3nlp-1.11},
year = {2023},
date = {2023-01-01},
pages = {107–113},
abstract = {The definitions of abusive, offensive, toxic and uncivil comments used for annotating corpora for automated content moderation are highly intersected and researchers call for their disambiguation. We summarize the definitions of these terms as they appear in 23 papers across different fields. We compare examples given for uncivil, offensive, and toxic comments, attempting to foster more unified scientific resources. Additionally, we stress that the term incivility that frequently appears in social science literature has hardly been mentioned in the literature we analyzed that focuses on computational linguistics and natural language processing.},
keywords = {},
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The definitions of abusive, offensive, toxic and uncivil comments used for annotating corpora for automated content moderation are highly intersected and researchers call for their disambiguation. We summarize the definitions of these terms as they appear in 23 papers across different fields. We compare examples given for uncivil, offensive, and toxic comments, attempting to foster more unified scientific resources. Additionally, we stress that the term incivility that frequently appears in social science literature has hardly been mentioned in the literature we analyzed that focuses on computational linguistics and natural language processing.
Prem, Erich; Neidhardt, Julia; Knees, Peter; Woltran, Stefan; Werthner, Hannes
Digital Humanism and Norms in Recommender Systems Proceedings Article
In: Vrijenhoek, Sanne; Michiels, Lien; Kruse, Johannes; Starke, Alain; Guerrero, Jordi Viader; Tintarev, Nava (Ed.): CEUR-WS.org, 2023.
@inproceedings{ 20.500.12708_211127,
title = {Digital Humanism and Norms in Recommender Systems},
author = {Erich Prem and Julia Neidhardt and Peter Knees and Stefan Woltran and Hannes Werthner},
editor = {Sanne Vrijenhoek and Lien Michiels and Johannes Kruse and Alain Starke and Jordi Viader Guerrero and Nava Tintarev},
doi = {10.34726/8560},
year = {2023},
date = {2023-01-01},
volume = {3639},
publisher = {CEUR-WS.org},
abstract = {Recommender systems can have a substantial impact on individual choices and on society as a whole. However, there is no clear set of norms for the design and use of recommender systems. This paper argues that Digital Humanism can provide a framework to foster more ethical and value-driven technology design. It emphasizes values such as human rights, democracy, and inclusion, and offers a critical perspective on implicit norms that typically guide system design. From this perspective, norms for recommender systems should strive to counteract information and power asymmetries, strengthen democratic societies, and protect users’ dignity, freedom, and self-determination. Users should be given more control over their data and over the recommendations that they receive. Future research directions are outlined, encompassing transparent optimization objectives, user empowerment in modeling, democratic control mechanisms, and strategies for ethical personalization.},
keywords = {},
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tppubtype = {inproceedings}
}
Recommender systems can have a substantial impact on individual choices and on society as a whole. However, there is no clear set of norms for the design and use of recommender systems. This paper argues that Digital Humanism can provide a framework to foster more ethical and value-driven technology design. It emphasizes values such as human rights, democracy, and inclusion, and offers a critical perspective on implicit norms that typically guide system design. From this perspective, norms for recommender systems should strive to counteract information and power asymmetries, strengthen democratic societies, and protect users’ dignity, freedom, and self-determination. Users should be given more control over their data and over the recommendations that they receive. Future research directions are outlined, encompassing transparent optimization objectives, user empowerment in modeling, democratic control mechanisms, and strategies for ethical personalization.
Kuflik, Tsvi; Kleanthous, Styliani; Neidhardt, Julia; Pera, Maria Soledad
UMAP 2023 Chairs’ Welcome Proceedings Article
In: pp. iii–vi, Association for Computing Machinery (ACM), New York, NY, USA, 2023.
@inproceedings{ 20.500.12708_210864,
title = {UMAP 2023 Chairs' Welcome},
author = {Tsvi Kuflik and Styliani Kleanthous and Julia Neidhardt and Maria Soledad Pera},
year = {2023},
date = {2023-01-01},
pages = {iii–vi},
publisher = {Association for Computing Machinery (ACM)},
address = {New York, NY, USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Modre, Laura; Neidhardt, Julia; Nalis, Irina
Value-Based Nudging in News Recommender Systems – Results From an Experimental User Study Proceedings Article
In: Proceedings of the First Workshop on the Normative Design and Evaluation of Recommender Systems (NORMalize 2023), 2023.
@inproceedings{modre2023value,
title = {Value-Based Nudging in News Recommender Systems – Results From an Experimental User Study},
author = {Laura Modre and Julia Neidhardt and Irina Nalis},
url = {https://ceur-ws.org/Vol-3639/paper4.pdf},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Proceedings of the First Workshop on the Normative Design and Evaluation of Recommender Systems (NORMalize 2023)},
volume = {3639},
series = {CEUR Workshop Proceedings},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2022
Hussak, Melanie; Neidhardt, Julia; Stilz, Melanie
Bildung für den Frieden in einer digitalisierten Welt Miscellaneous
2022.
@misc{ 20.500.12708_153931,
title = {Bildung für den Frieden in einer digitalisierten Welt},
author = {Melanie Hussak and Julia Neidhardt and Melanie Stilz},
year = {2022},
date = {2022-01-01},
abstract = {In Artikel 4 der Erklärung der UN-Generalversammlung über eine Kultur des Friedens steht:
Bildung auf allen Ebenen ist eines der wichtigsten Instrumente zum Aufbau einer Kultur des Friedens. Dabei kommt der Menschenrechtserziehung eine besondere Bedeutung zu.
Frieden ist ein nie abgeschlossenes Projekt, das auf die stetige Abnahme von Gewalt und die gleichzeitige Zunahme von Gerechtigkeit zielt. Die Friedenspädagogik als inter- und transdisziplinäre Wissenschaft besitzt den weiten Blick, das Potential der Informatik für den “Ewigen Frieden” (Kant) in einer Paneldiskussion anschaulich und konkret darzustellen.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
In Artikel 4 der Erklärung der UN-Generalversammlung über eine Kultur des Friedens steht:
Bildung auf allen Ebenen ist eines der wichtigsten Instrumente zum Aufbau einer Kultur des Friedens. Dabei kommt der Menschenrechtserziehung eine besondere Bedeutung zu.
Frieden ist ein nie abgeschlossenes Projekt, das auf die stetige Abnahme von Gewalt und die gleichzeitige Zunahme von Gerechtigkeit zielt. Die Friedenspädagogik als inter- und transdisziplinäre Wissenschaft besitzt den weiten Blick, das Potential der Informatik für den “Ewigen Frieden” (Kant) in einer Paneldiskussion anschaulich und konkret darzustellen.
Bildung auf allen Ebenen ist eines der wichtigsten Instrumente zum Aufbau einer Kultur des Friedens. Dabei kommt der Menschenrechtserziehung eine besondere Bedeutung zu.
Frieden ist ein nie abgeschlossenes Projekt, das auf die stetige Abnahme von Gewalt und die gleichzeitige Zunahme von Gerechtigkeit zielt. Die Friedenspädagogik als inter- und transdisziplinäre Wissenschaft besitzt den weiten Blick, das Potential der Informatik für den “Ewigen Frieden” (Kant) in einer Paneldiskussion anschaulich und konkret darzustellen.